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diff --git a/handbook/.ipynb_checkpoints/index-checkpoint.md b/handbook/.ipynb_checkpoints/index-checkpoint.md deleted file mode 100644 index f1e2e91..0000000 --- a/handbook/.ipynb_checkpoints/index-checkpoint.md +++ /dev/null @@ -1,38 +0,0 @@ - - -__PoF in a Box provides a ready-to-run set of notebooks designed to help you get started with your own Predictability of Fire (PoF) experiments. It includes all the tools, data access, and workflows needed to explore fire activity prediction, from input preparation to model evaluation—right out of the box.__ - - -# Wildfire prediction - -Given their chaotic nature, how can we know when and where wildfires will occur? That is a question that has puzzled wildfire forecasters for decades and now, thanks to advances in **machine learning**, we are one step closer to answering it, although we still have some way to go. - -The topic of **wildfire forecasting** is not a new one. However, in recent years media attention around the subject has grown as unprecedented wildfire seasons in Australia (2019/2020) and Canada (2023) and Europe (2025) have resulted in widespread devastation of local ecosystems and communities. - -These events have far-reaching consequences for both air quality and greenhouse gas emissions - -## Traditional fire forecasting -For nearly half a century, fire danger forecasts have relied on a method that links weather conditions with fire activity to create an index of fire risk, with the **Canadian Fire Weather Index (FWI)** - being the most widely used. However, this approach has its limitations. -  - -**A fire needs fuel**, and for wildfires that fuel is both **living** and **dead** vegetation. The abundance and arrangement of that fuel is known as the ‘**fuel bed**’. If all else is equal, the drier the fuel bed, the higher the fire risk. The FWI primarily estimates the state of fuel moisture based on meteorological conditions affecting a predetermined typical Canadian forest fuel bed. However, a typical fuel bed does not capture controls such as the moisture levels in living vegetation, the composition of vegetation types, or the actual abundance of available fuel. Consequently, the FWI tends to overestimate fire risk in areas with limited fuel. Moreover, because the FWI was originally developed for Canadian forests, its applicability becomes complex when extrapolated to different ecosystems. - -All this is before we even consider the factors that might start a wildfire in the first place (known as ignitions). Ninety per cent of ignitions are caused by the unpredictable behaviour of humans, making them chaotic in nature and hard to predict. - -```{tip} -These shortcomings highlight the need for more advanced forecasting techniques to accurately assess wildfire risk across diverse landscapes. -``` - -# Machine learning evolution - -In recent months, there has been a remarkable growth in the integration of machine learning into weather forecasting systems. **With the intricate dynamics governing wildfires, it seems only natural to explore similar applications of machine learning in fire forecasting.** - -We have developed a new tool, known as **Probability of Fire, or PoF,** which uses machine learning techniques to effectively forecast fire occurrence globally at high resolution, up to ten days in advance. -```{tip} -A key strength of PoF is found not only in the accurate predictions but also in computational cost. The model itself is extremely cheap to run compared with more traditional physical models, which allows us to perform global 1 km forecasts. -``` -  -The foundation of PoF lies in its utilisation of diverse datasets, including information from the ECMWF Integrated Forecasting System (IFS), land cover data, and a **newly developed fuel characteristic model**. - -The training of PoF is made possible thanks to the wealth of historic observations of active fires from satellites. The model mimics what it anticipates satellites will detect in the next few days. Consequently, it could only ever hope to perform as well as the satellite, which emphasises the importance of accurate satellite data for training. diff --git a/handbook/PoF_Data_Generator.ipynb b/handbook/POF_Data_Generator.ipynb similarity index 60% rename from handbook/PoF_Data_Generator.ipynb rename to handbook/POF_Data_Generator.ipynb index f8979ae..47120a8 100755 --- a/handbook/PoF_Data_Generator.ipynb +++ b/handbook/POF_Data_Generator.ipynb @@ -1,9 +1,38 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# 💿 PoF Data Generator" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "Importing all the necessary python libraries\n", + "before we begin, we must prepare our enviroment. This includes installing the Application Programming Interface (API) of the CDS, and importing the various python libraries we will need.\n", + "\n", + "These should have been installed into a conda environment before running the Notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q cdsapi" + ] + }, { "cell_type": "code", - "execution_count": 1, - "id": "ce6f7bca", + "execution_count": null, + "id": "3", "metadata": {}, "outputs": [], "source": [ @@ -14,13 +43,23 @@ "from pathlib import Path\n", "import cdsapi\n", "import os\n", - "import zipfile\n" + "import zipfile\n", + "import requests\n" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "Here we will define some configuration options. \n", + "For the purpose of the book we try to keep the volume of data small. This will involve downloading data from the CDS." ] }, { "cell_type": "code", - "execution_count": 2, - "id": "22ddcd71", + "execution_count": null, + "id": "5", "metadata": {}, "outputs": [], "source": [ @@ -32,29 +71,103 @@ "cds_days= [f\"{d:02d}\" for d in range(1, 32)]" ] }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "ECBox Data Download\n", + "\n", + "Some of the data we will use is available on an EC Box at ECMWF\n", + "We have provided the download here to retrieve the files needed for the Climate and for Active Fire Data." + ] + }, { "cell_type": "code", - "execution_count": 3, - "id": "616ed1ac", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "os.makedirs(\"data\", exist_ok=True)\n", + "os.makedirs(\"data/CLIMATE\", exist_ok=True)\n", + "def retrieve_file(path):\n", + " \n", + " output_path=f\"data/{path}\"\n", + " if not os.path.exists(output_path):\n", + " username = \"ecbox\"\n", + " token = \"1vXbp0THtxZkla015skARAqTnf2DxxoQS0qlwrgpKiIGpPRKLtXklYO39tOJTXtnZrxP16WZs99iSoFgTTAy3orpmjykOBAO7CVk5RQIWFvZFhiSv0Ev3O3\"\n", + "\n", + " base_url = \"https://sites.ecmwf.int/ecbox/POF_IN_A_BOX/s/dav/data/\"\n", + "\n", + " response = requests.get(f'{base_url}/{path}', auth=(username, token))\n", + "\n", + " if response.status_code == 200:\n", + " with open(f\"data/{path}\", \"wb\") as f:\n", + " f.write(response.content)\n", + " # print(\"Downloaded!\")\n", + " else:\n", + " print(response.status_code, response.text)\n", + "\n", + "for year in range(2003, 2022):\n", + " for month in range(1, 13):\n", + " retrieve_file(f\"ACTIVE_FIRE_MAP_{year}_{month:02d}_R.nc\")\n", + " \n", + "retrieve_file(f\"CLIMATE/POP_2020.nc\")\n", + "retrieve_file(f\"CLIMATE/road_density_2015_agg_r.nc\")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "##Enter your CDS API key and XDS-Preprod API Key\n", + "\n", + "We will request data from the Climate Data Store (CDS) and the Cross Data Store (XDS) programmatically with the help of the CDS API.\n", + "\n", + "If you have not set up your CDS API credentials with a ~/.cdsapirc file, it is possible to provide the credentials when initialising the cdsapi.Client. To do this we must define the two variables needed to establish a connection: URL and KEY . The URL for the cds api is common and you do not need to modify that field. The KEY is string of characters made up of your your personal User ID and CDS API key. To obtain these, first register or login to the CDS (https://cds.climate.copernicus.eu), then visit https://cds.climate.copernicus.eu/how-to-api and copy the string of characters listed after \"key:\". Replace the ######### below with this string.\n", + "\n", + "For XDS the URL and instance are different as so we need the a different set of credentials for XDS. The API functions in the same way, but you will need to register and login on the XDS (https://xds-preprod.ecmwf.int/) and retrieve your API key here: https://xds-preprod.ecmwf.int/how-to-api \n", + "\n", + "NOTE: If you have set up your cdsapi key using a ~/.cdsapirc you will need to update it for using the xds key or override it as we have demonstrated below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", "metadata": {}, "outputs": [], "source": [ "\n", "cds_kwargs = {\n", " 'url': 'https://cds.climate.copernicus.eu/api',\n", - " 'key': '14e44bc7-0f33-4a57-847f-f41f44c5ec4d',\n", + " 'key': '#####################################',\n", "}\n", "\n", "xds_kwargs = {\n", " 'url': 'https://xds-preprod.ecmwf.int/api',\n", - " 'key': '081c061b-d9b9-42eb-a483-a78e2b61cfd1',\n", + " 'key': '#####################################',\n", "}" ] }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "CDS Data Retrieval\n", + "\n", + "This code block will retrieve the datasets needed from the CDS including temperature, wind speed, precipidation and dewpoint. We will retreive for the years and months that were configured above.\n", + "\n", + "We require wind speed, we will calculate this from the 10U and 10V wind components." + ] + }, { "cell_type": "code", - "execution_count": 4, - "id": "4e2a9617", + "execution_count": null, + "id": "11", "metadata": {}, "outputs": [], "source": [ @@ -91,13 +204,32 @@ " dsw=dsw.rename(\"ws\")\n", " dsw.to_netcdf(f\"data/WS_{year}_{month}.nc\")\n", " del dsw\n", - "\n" + "\n", + " if not os.path.exists(f\"data/RH_{year}_{month}.nc\"):\n", + " with xr.open_dataset(f\"data/T2M_{year}_{month}.nc\") as dst,\\\n", + " xr.open_dataset(f\"data/D2M_{year}_{month}.nc\") as dsd:\n", + " es = 10**(7.5*(dst.t2m-273.15)/(237.7+(dst.t2m-273.15))) * 6.11\n", + " e = 10**(7.5*(dsd.d2m-273.15)/(237.7+(dsd.d2m-273.15))) * 6.11\n", + " dsrh = (e/es)*100\n", + " dsrh = dsrh.rename(\"rh\")\n", + " dsrh.to_netcdf(f\"data/RH_{year}_{month}.nc\")\n", + " del dsrh" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "We will now retrieve some data from the XDS.\n", + "\n", + "The XDS data is stored at a different grid to ERA5 Land and so we will regrid the data on the fly once it is downloaded." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "6d6731ce", + "execution_count": null, + "id": "13", "metadata": { "vscode": { "languageId": "shellscript" @@ -124,7 +256,7 @@ " ],\n", " \"version\": [\"1\"],\n", " \"year\": [\n", - " year\n", + " f'{year}'\n", " ],\n", " \"month\": [\n", " month\n", @@ -144,41 +276,30 @@ " del ds_regrid" ] }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "Build an XGBoost-compatible training dataset by combining\n", + "climate, vegetation, and anthropogenic data from multiple NetCDF sources." + ] + }, { "cell_type": "code", - "execution_count": 6, - "id": "7a12a1ca", + "execution_count": null, + "id": "15", "metadata": { "vscode": { "languageId": "shellscript" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading static datasets...\n", - "Processing year 2003...\n", - "Combining sampled data...\n", - "Saving dataset → ./data/training_data.parquet\n", - "✅ Training dataset ready.\n" - ] - } - ], + "outputs": [], "source": [ - "\"\"\"\n", - "Build an XGBoost-compatible training dataset by combining\n", - "climate, vegetation, and anthropogenic data from multiple NetCDF sources.\n", - "\"\"\"\n", - "\n", - "# -----------------------------\n", - "# CONFIG & CONSTANTS\n", - "# -----------------------------\n", "output_path=\"./data/training_data.parquet\"\n", "base_path = Path(\"./data/\")\n", "\n", - "sample_frac = 1 / 100 # keep ~10% of total samples\n", + "sample_frac = 1 # keep all data for training.\n", "\n", "# -----------------------------\n", "# LOAD STATIC DATASETS\n", @@ -186,11 +307,9 @@ "time_coder = CFDatetimeCoder(use_cftime=True)\n", "\n", "print(\"Loading static datasets...\")\n", - "PO = xr.open_dataset(base_path / \"CLIMATE/POP_2020.nc_2\")\n", - "UR = xr.open_dataset(base_path / \"CLIMATE/urban_C.nc_2\", decode_times=time_coder)\n", - "RD = xr.open_dataset(base_path / \"CLIMATE/road_density_2015_c.nc_2\")\n", + "PO = xr.open_dataset(base_path / \"CLIMATE/POP_2020.nc\")\n", + "RD = xr.open_dataset(base_path / \"CLIMATE/road_density_2015_agg_r.nc\")\n", "\n", - "ur = UR.vegdiff.squeeze().values.flatten()\n", "po = PO.population_density.values.flatten()\n", "rd = RD.road_length.values.flatten()\n", "\n", @@ -213,8 +332,8 @@ " \"LF\": f\"LFMC_MAP_{year}_{mon}_R.nc\",\n", " \"PR\": f\"P_{year}_{mon}.nc\",\n", " \"T2\": f\"T2M_{year}_{mon}.nc\",\n", - " \"D2\": f\"D2M_{year}_{mon}.nc\",\n", - " \"WS\": f\"WS_{year}_{mon}.nc\",\n", + " \"RH\": f\"RH_{year}_{mon}.nc\",\n", + " \"WS\": f\"WS_{year}_{mon}.nc\"\n", " }\n", "\n", " # Load datasets efficiently with context managers\n", @@ -224,9 +343,9 @@ " xr.open_dataset(base_path / ds_paths[\"LF\"]) as LF, \\\n", " xr.open_dataset(base_path / ds_paths[\"PR\"]) as PR, \\\n", " xr.open_dataset(base_path / ds_paths[\"T2\"]) as T2, \\\n", - " xr.open_dataset(base_path / ds_paths[\"D2\"]) as D2, \\\n", + " xr.open_dataset(base_path / ds_paths[\"RH\"]) as RH, \\\n", " xr.open_dataset(base_path / ds_paths[\"WS\"]) as WS:\n", - "\n", + " AF=AF.fillna(0) # Fill NaNs in ACTIVE_FIRE with 0\n", " days = len(AF.ACTIVE_FIRE)\n", "\n", " # Process only the first day (change np.arange(1) to range(len(AF.ACTIVE_FIRE)) if needed)\n", @@ -241,7 +360,7 @@ " lf = LF.LFMC[i].values.flatten()\n", " pr = PR.tp[i].values.flatten()\n", " t2 = T2.t2m[i].values.flatten()\n", - " d2 = D2.d2m[i].values.flatten()\n", + " rh = RH.rh[i].values.flatten()\n", " ws = WS.ws[i].values.flatten()\n", "\n", " # Mask where total fuel > 0\n", @@ -253,7 +372,7 @@ " \"AF\": af[mask],\n", " \"PR\": pr[mask],\n", " \"T2\": t2[mask],\n", - " \"D2\": d2[mask],\n", + " \"RH\": rh[mask],\n", " \"WS\": ws[mask],\n", " \"FU_LL\": fu_ll[mask],\n", " \"FU_LW\": fu_lw[mask],\n", @@ -262,7 +381,6 @@ " \"DF\": df[mask],\n", " \"DW\": dw[mask],\n", " \"LF\": lf[mask],\n", - " \"UR\": ur[mask],\n", " \"PO\": po[mask],\n", " \"RD\": rd[mask],\n", " }, dtype=float)\n", @@ -295,7 +413,7 @@ ], "metadata": { "kernelspec": { - "display_name": "POF_IN_A_BOX", + "display_name": "Python 3.12.11-01", "language": "python", "name": "python3" }, @@ -309,7 +427,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.0" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/handbook/POF_Forecast.ipynb b/handbook/POF_Forecast.ipynb new file mode 100755 index 0000000..5aeafb1 --- /dev/null +++ b/handbook/POF_Forecast.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# POF Forecast" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "Importing all the necessary python libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import numpy as np\n", + "import pandas as pd\n", + "import joblib\n", + "from pathlib import Path\n", + "import cftime\n", + "from xarray.coding.times import CFDatetimeCoder\n", + "import os\n", + "import cdsapi\n", + "import zipfile\n" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "Configuration Options\n", + "\n", + "Here we define the paths for files and the dates we will use for our forecast.\n", + "\n", + "We also load the model we created in the Trainer notebook\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "# Folder structure\n", + "base_path = Path(\"./data/\")\n", + "os.makedirs(\"./outputs/\", exist_ok=True)\n", + "\n", + "# Dates of Forecast\n", + "year = 2019\n", + "years=[year]\n", + "months = [12]\n", + "cds_months=[f'{m:02d}' for m in months]\n", + "cds_days= [f\"{d:02d}\" for d in range(1, 32)]\n", + "\n", + "# Where we will store our predictions\n", + "output_file = f\"./outputs/POF_prediction_{year}_{month:02d}.nc\"\n", + "\n", + "# Load trained model\n", + "model = joblib.load(\"./data/POF_model.joblib\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "cds_kwargs = {\n", + " 'url': 'https://cds.climate.copernicus.eu/api',\n", + " 'key': '14e44bc7-0f33-4a57-847f-f41f44c5ec4d',\n", + "}\n", + "\n", + "xds_kwargs = {\n", + " 'url': 'https://xds-preprod.ecmwf.int/api',\n", + " 'key': '081c061b-d9b9-42eb-a483-a78e2b61cfd1',\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "for year in years:\n", + " for month in cds_months: \n", + " for shortname,var in [[\"T2M\",\"2m_temperature\"],\n", + " [\"D2M\",\"2m_dewpoint_temperature\"],\n", + " [\"10U\",\"10m_u_component_of_wind\"],\n", + " [\"10V\",\"10m_v_component_of_wind\"],\n", + " [\"P\",\"total_precipitation\"]]:\n", + "\n", + " dataset = \"reanalysis-era5-land\"\n", + " request = {\n", + " \"variable\": [\n", + " var,\n", + " ],\n", + " \"year\": year,\n", + " \"month\": month,\n", + " \"day\": cds_days,\n", + " \"time\": [\n", + " \"00:00\"\n", + " ],\n", + " \"data_format\": \"netcdf\",\n", + " \"download_format\": \"unarchived\"\n", + " }\n", + " client = cdsapi.Client(**cds_kwargs)\n", + " if not os.path.exists(f\"data/{shortname}_{year}_{month}.nc\"):\n", + " client.retrieve(dataset, request, target=f\"data/{shortname}_{year}_{month}.nc\")\n", + " if not os.path.exists(f\"data/WS_{year}_{month}.nc\"):\n", + " with xr.open_dataset(f\"data/10U_{year}_{month}.nc\") as dsu,\\\n", + " xr.open_dataset(f\"data/10V_{year}_{month}.nc\") as dsv:\n", + " dsw=dsu.u10**2+ dsv.v10**2\n", + " dsw=dsw.rename(\"ws\")\n", + " dsw.to_netcdf(f\"data/WS_{year}_{month}.nc\")\n", + " del dsw\n", + "\n", + " if not os.path.exists(f\"data/RH_{year}_{month}.nc\"):\n", + " with xr.open_dataset(f\"data/T2M_{year}_{month}.nc\") as dst,\\\n", + " xr.open_dataset(f\"data/D2M_{year}_{month}.nc\") as dsd:\n", + " es = 10**(7.5*(dst.t2m-273.15)/(237.7+(dst.t2m-273.15))) * 6.11\n", + " e = 10**(7.5*(dsd.d2m-273.15)/(237.7+(dsd.d2m-273.15))) * 6.11\n", + " dsrh = (e/es)*100\n", + " dsrh = dsrh.rename(\"rh\")\n", + " dsrh.to_netcdf(f\"data/RH_{year}_{month}.nc\")\n", + " del dsrh" + ] + }, + { + "cell_type": "markdown", + "id": "7", + "metadata": {}, + "source": [ + "This section will download data from the XDS-Preprod.\n", + "\n", + "The data is large and so may take some time to download." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "lat_lon_data = xr.open_dataset(f\"data/T2M_{years[0]}_{cds_months[0]}.nc\").sortby(\"latitude\").sortby(\"longitude\")\n", + "lat_lon_data=lat_lon_data.rename(valid_time=\"time\",latitude='lat',longitude='lon')\n", + "lat_lon_data=lat_lon_data.drop_vars(['expver','number'])\n", + "\n", + "for year in years:\n", + " for month in cds_months: \n", + " for shortname, var in \\\n", + " [[\"DFMC_MAP\",\"dead_fuel_moisture_content_group\"],\n", + " [\"LFMC_MAP\",\"live_fuel_moisture_content_group\"],\n", + " [\"FUEL_MAP\",\"fuel_group\"]]:\n", + "\n", + "\n", + " dataset = \"derived-fire-fuel-biomass\"\n", + " request = {\n", + " \"variable\": [\n", + " var,\n", + " ],\n", + " \"version\": [\"1\"],\n", + " \"year\": [\n", + " f'{year}'\n", + " ],\n", + " \"month\": [\n", + " month\n", + " ]\n", + " }\n", + "\n", + " client = cdsapi.Client(**xds_kwargs)\n", + " if not os.path.exists(f\"data/{shortname}_{year}_{month}_R.nc\"):\n", + " client.retrieve(dataset, request, target=f\"data/{shortname}_{year}_{month}.zip\")\n", + "\n", + " with zipfile.ZipFile(f\"data/{shortname}_{year}_{month}.zip\", 'r') as zip_ref:\n", + " zip_ref.extractall(\"data/\")\n", + " print(f\"Regridding {shortname} for {year}-{month}\")\n", + " with xr.open_dataset(f\"data/{shortname}_{year}_{month}.nc\") as ds_temp:\n", + " ds_regrid=ds_temp.interp_like(lat_lon_data, method=\"linear\")\n", + " ds_regrid.to_netcdf(f\"data/{shortname}_{year}_{month}_R.nc\")\n", + " del ds_regrid" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": [ + "Load Static Data\n", + "\n", + "We will load some CLIMATE data files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "# Open the Climate files\n", + "time_coder = CFDatetimeCoder(use_cftime=True)\n", + "PO = xr.open_dataset(base_path / \"CLIMATE/POP_2020.nc\")\n", + "RD = xr.open_dataset(base_path / \"CLIMATE/road_density_2015_agg_r.nc\")\n", + "\n", + "# Extract the arrays\n", + "PO_arr = PO.population_density.values \n", + "RD_arr = RD.road_length.values " + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "Load Dynamic Data\n", + "\n", + "Here we open the data files we retrieved from the XDS and the CDS as well as the Active Fire Maps\n", + "\n", + "We iterate through each day within the dataset and store the into an output NetCDF file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "for year in years:\n", + " for month in months:\n", + " # Define the paths of the files\n", + " ds_paths = {\n", + " \"AF\": f\"ACTIVE_FIRE_MAP_{year}_{month:02d}_R.nc\",\n", + " \"FU\": f\"FUEL_MAP_{year}_{month:02d}_R.nc\",\n", + " \"DF\": f\"DFMC_MAP_{year}_{month:02d}_R.nc\",\n", + " \"LF\": f\"LFMC_MAP_{year}_{month:02d}_R.nc\",\n", + " \"PR\": f\"P_{year}_{month:02d}.nc\",\n", + " \"T2\": f\"T2M_{year}_{month:02d}.nc\",\n", + " \"RH\": f\"RH_{year}_{month:02d}.nc\",\n", + " \"WS\": f\"WS_{year}_{month:02d}.nc\",\n", + " }\n", + " # Open all dynamic datasets\n", + " with xr.open_dataset(base_path / ds_paths[\"FU\"]) as FU, \\\n", + " xr.open_dataset(base_path / ds_paths[\"DF\"]) as DF, \\\n", + " xr.open_dataset(base_path / ds_paths[\"LF\"]) as LF, \\\n", + " xr.open_dataset(base_path / ds_paths[\"PR\"]) as PR, \\\n", + " xr.open_dataset(base_path / ds_paths[\"T2\"]) as T2, \\\n", + " xr.open_dataset(base_path / ds_paths[\"RH\"]) as RH, \\\n", + " xr.open_dataset(base_path / ds_paths[\"WS\"]) as WS, \\\n", + " xr.open_dataset(base_path / ds_paths[\"AF\"]) as AF:\n", + "\n", + " n_days = len(AF.ACTIVE_FIRE)\n", + " all_grids = []\n", + "\n", + " for i in range(n_days):\n", + " # Extract arrays for timestep i\n", + " FU_LL = FU.Live_Leaf[i].values\n", + " FU_LW = FU.Live_Wood[i].values\n", + " FU_DF = FU.Dead_Foliage[i].values\n", + " FU_DW = FU.Dead_Wood[i].values\n", + " DF_ = DF.DFMC_Foliage[i].values\n", + " DW_ = DF.DFMC_Wood[i].values\n", + " LF_ = LF.LFMC[i].values\n", + " PR_ = PR.tp[i].values\n", + " T2_ = T2.t2m[i].values\n", + " RH_ = RH.rh[i].values\n", + " WS_ = WS.ws[i].values\n", + "\n", + " # Mask where total fuel > 0\n", + " ft = FU_LL + FU_LW + FU_DF + FU_DW\n", + " mask = ft > 0\n", + "\n", + " # Flatten and build dataframe for prediction\n", + " feature_arrays = {\n", + " \"PR\": PR_[mask],\n", + " \"T2\": T2_[mask],\n", + " \"RH\": RH_[mask],\n", + " \"WS\": WS_[mask],\n", + " \"FU_LL\": FU_LL[mask],\n", + " \"FU_LW\": FU_LW[mask],\n", + " \"FU_DF\": FU_DF[mask],\n", + " \"FU_DW\": FU_DW[mask],\n", + " \"DF\": DF_[mask],\n", + " \"DW\": DW_[mask],\n", + " \"LF\": LF_[mask],\n", + " \"PO\": PO_arr[mask],\n", + " \"RD\": RD_arr[mask],\n", + " }\n", + " X_pred = pd.DataFrame(feature_arrays)\n", + "\n", + " # Predict probability\n", + " y_proba = model.predict_proba(X_pred)[:, 1]\n", + "\n", + " # Create full grid and fill masked values\n", + " fire_prob_grid = np.full(ft.shape, np.nan, dtype=float)\n", + " fire_prob_grid[mask] = y_proba\n", + " all_grids.append(fire_prob_grid)\n", + "\n", + " # Stack all timesteps into 3D array (time, lat, lon)\n", + " fire_prob_array = np.stack(all_grids, axis=0)\n", + " \n", + " # -----------------------------\n", + " # SAVE TO NETCDF\n", + " # -----------------------------\n", + " time = [cftime.DatetimeJulian(year, month, day+1) for day in range(n_days)]\n", + "\n", + " ds_out = xr.Dataset(\n", + " {\"fire_probability\": ([\"time\", \"latitude\", \"longitude\"], fire_prob_array)},\n", + " coords={\n", + " \"time\": time,\n", + " \"latitude\": PO.latitude,\n", + " \"longitude\": PO.longitude,\n", + " }\n", + " )\n", + " os.remove(output_file) if os.path.exists(output_file) else None\n", + " ds_out.to_netcdf(output_file)\n", + " print(f\"✅ Prediction saved → {output_file}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "month" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.12.11-01", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/handbook/POF_Trainer.ipynb b/handbook/POF_Trainer.ipynb new file mode 100755 index 0000000..71ced13 --- /dev/null +++ b/handbook/POF_Trainer.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "To evaluate and compare multiple machine learning strategies, we tested several model configurations, including gradient-boosted decision trees, random forests, and neural networks. \n", + "Among these, the **XGBoost-based approach** demonstrated the best overall performance and computational efficiency. For this reason the PoF system uses gradient-boosted decision trees that iteratively improve prediction accuracy by correcting errors made in previous iterations. We found that this method provides an efficient optimization framework for large-scale, global applications. \n", + "\n", + "The model is trained using a classifier setup, where a positive event is defined as the occurrence of an active fire detection within a grid cell on a given day. \n", + "```{note} \n", + "While tree-based models do not explicitly represent temporal dependencies, this limitation is mitigated through input features that encode environmental memory—such as live fuel moisture, vegetation state, and soil moisture—capturing information about preceding conditions. \n", + "```\n", + "\n", + "\n", + "Overall, the XGBoost configuration provided the most robust and interpretable results, balancing accuracy, computational cost, and operational suitability.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import time\n", + "import joblib\n", + "import matplotlib.pyplot as plt\n", + "import xgboost as xgb\n", + "from xgboost import XGBClassifier, plot_importance\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, RocCurveDisplay\n" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "Load your data created from the previous notebook\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "df = pd.read_parquet(\"./data/training_data.parquet\")\n", + "\n", + "print(\"Loaded data shape:\", df.shape)\n", + "print(\"Columns:\", list(df.columns))" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "\n", + "Define your features and target" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "target_col = \"AF\"\n", + "feature_cols = [c for c in df.columns if c not in [\"AF\"]]\n", + "\n", + "X = df[feature_cols]\n", + "y = df[target_col]\n" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "```{note}🔀 Train–Test Split\n", + "\n", + "The dataset is divided into training and testing subsets to evaluate model performance on unseen data. \n", + "We use an **80/20 split**, meaning 80% of the samples are used for training and 20% are reserved for testing. \n", + "\n", + "To ensure reproducibility, the split is controlled by a fixed random seed (`random_state=42`). \n", + "Additionally, **stratification** is applied (`stratify=y`) so that the proportion of active fire and non-fire samples remains consistent across both training and testing subsets. \n", + "This helps maintain balanced class representation and prevents bias during model evaluation. \n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=42, stratify=y\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "min(y_train)" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": [ + "| Parameter | Description |\n", + "| --------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n", + "| **`objective=\"binary:logistic\"`** | Specifies that this is a **binary classification** problem. The model outputs probabilities between 0 and 1 using a logistic function. |\n", + "| **`tree_method=\"hist\"`** | Uses the **histogram-based algorithm** to speed up training, especially for large datasets. It groups feature values into discrete bins instead of processing each unique value. |\n", + "| **`n_estimators=300`** | The number of **boosting rounds (trees)** to build. Higher values can improve accuracy but may increase training time or risk overfitting. |\n", + "| **`max_depth=8`** | Maximum depth of each decision tree. Deeper trees can model more complex patterns but may overfit the data. |\n", + "| **`learning_rate=0.1`** | Shrinks the contribution of each tree. Lower values make learning slower but more robust; typically balanced with `n_estimators`. |\n", + "| **`subsample=0.8`** *(optional)* | Fraction of the training data used for each tree. Prevents overfitting and increases generalization. Default is `1.0` (use all data). |\n", + "| **`colsample_bytree=0.8`** *(optional)* | Fraction of features (columns) randomly sampled for each tree, adding diversity and reducing overfitting. |\n", + "| **`eval_metric=\"logloss\"`** | Evaluation metric for binary classification. Measures the model’s prediction accuracy in terms of probability calibration (lower is better). |\n", + "| **`random_state=42`** *(optional)* | Sets the random seed for reproducibility. Ensures consistent results when running the code multiple times. |\n", + "| **`n_jobs=-1`** | Uses **all available CPU cores** for parallel computation, speeding up training. |\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# -----------------------------\n", + "# TRAIN CLASSIFIER\n", + "# -----------------------------\n", + "print(\"Training XGBoost Binary Classifier...\")\n", + "start_time = time.time()\n", + "\n", + "model = XGBClassifier(\n", + " objective=\"binary:logistic\",\n", + " tree_method=\"hist\",\n", + " n_estimators=300,\n", + " max_depth=8,\n", + " learning_rate=0.1,\n", + "# subsample=0.8,\n", + "# colsample_bytree=0.8,\n", + " eval_metric=\"logloss\",\n", + "# random_state=42,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "model.fit(X_train, y_train)\n", + "\n", + "end_time = time.time()\n", + "print(f\"✅ Training completed in {end_time - start_time:.2f} seconds\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# -----------------------------\n", + "# EVALUATE MODEL\n", + "# -----------------------------\n", + "y_pred = model.predict(X_test)\n", + "y_proba = model.predict_proba(X_test)[:, 1]\n", + "\n", + "print(\"\\nClassification report:\")\n", + "print(classification_report(y_test, y_pred, digits=3))\n", + "\n", + "roc_auc = roc_auc_score(y_test, y_proba)\n", + "print(f\"ROC-AUC: {roc_auc:.3f}\")\n", + "\n", + "# Confusion matrix\n", + "print(\"\\nConfusion Matrix:\")\n", + "print(confusion_matrix(y_test, y_pred))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# -----------------------------\n", + "# PLOT ROC CURVE & FEATURE IMPORTANCE\n", + "# -----------------------------\n", + "os.makedirs(\"./outputs\", exist_ok=True)\n", + "plt.figure(figsize=(6, 5))\n", + "RocCurveDisplay.from_estimator(model, X_test, y_test)\n", + "plt.title(\"ROC Curve\")\n", + "plt.savefig(\"./outputs/POF_ROC.png\", dpi=300)\n", + "plt.show()\n", + "plt.close()\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plot_importance(model, max_num_features=20, importance_type=\"gain\")\n", + "plt.title(\"Feature Importance (Gain)\")\n", + "plt.tight_layout()\n", + "plt.savefig(\"./outputs/POF_importance.png\", dpi=300)\n", + "plt.show()\n", + "plt.close()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# -----------------------------\n", + "# SAVE MODEL\n", + "# -----------------------------\n", + "out_model = \"./data/POF_model.joblib\"\n", + "joblib.dump(model, out_model, compress=3)\n", + "print(f\"Model saved → {out_model}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "authors": [ + { + "name": "Christopher Barnard" + }, + { + "name": "Joe McNorton" + }, + { + "name": "Francesca Di Giuseppe" + } + ], + "kernelspec": { + "display_name": "Python 3.12.11-01", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + }, + "show-body-title": false, + "title": "🗂️ Training Dataset Summary" + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/handbook/POF_plotter.ipynb b/handbook/POF_plotter.ipynb new file mode 100644 index 0000000..381aec1 --- /dev/null +++ b/handbook/POF_plotter.ipynb @@ -0,0 +1,221 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# POF Plotter" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "Importing all the necessary python libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import colors\n", + "from matplotlib import cm\n", + "from matplotlib.colors import Normalize\n", + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib as mpl\n", + "from cartopy import config\n", + "import cartopy.crs as ccrs\n", + "import cartopy as cart\n", + "import sys\n", + "import datetime\n", + "from matplotlib.colors import ListedColormap,LinearSegmentedColormap\n" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "Open Prediction Netcdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "year=2019\n", + "month=12\n", + "\n", + "\n", + "fire= xr.open_dataset(f\"./outputs/POF_prediction_{year}_{month:02d}.nc\")\n", + "fire = fire.assign_coords(longitude=((fire.longitude + 360) % 360)).sortby('longitude')\n", + "lats = fire.latitude.values\n", + "lons = fire.longitude.values\n", + "fire\n" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "Define a subregion to plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "lat1 = -10\n", + "lat2 = -45\n", + "lon1 = 110\n", + "lon2 = 155\n", + "\n", + "sub_ds = fire.sel(\n", + " latitude=slice(lat1, lat2), \n", + " longitude=slice(lon1, lon2)\n", + ")\n", + "# x1 = (np.argmin(np.abs(lons - lon1)))\n", + "# x2 = (np.argmin(np.abs(lons - lon2)))\n", + "# y1 = (np.argmin(np.abs(lats - lat1)))\n", + "# y2 = (np.argmin(np.abs(lats - lat2)))\n", + "\n", + "# sub_lons = lons[x1:x2]\n", + "# sub_lats = lats[y2:y1]\n", + "# print(y2)\n", + "# print(y1)\n", + "# print(sub_lats)\n", + "sub_ds\n" + ] + }, + { + "cell_type": "markdown", + "id": "7", + "metadata": {}, + "source": [ + "Create colour pallete" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "cols = ['#4a5a60','#edcc00','#edcc00','#e57d0f','#e57d0f','#e57d0f','#e57d0f','#e57d0f','#e57d0f','#e21819','#e21819','#e21819','#e21819','#e21819','#000000','#000000']\n", + "test = ListedColormap(cols)" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": [ + "Define levels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "index=0 # Which index to plot (1st of the month)\n", + "\n", + "fires = sub_ds.fire_probability[index].values\n", + "plot_title = f\"Probability of Fire Forecast for {sub_ds.time.values[index].strftime('%d %B %Y') }\"\n", + "\n", + "fires[fires>0.006]=0.006 # 1km\n", + "\n", + "levels = np.arange(16)/2500 # 1km\n", + "\n", + "print(np.shape(fires))\n", + "print(np.nanmax(fire.fire_probability.values))\n", + "print(np.nanmin(fire.fire_probability.values))\n", + "print(np.nanmean(fire.fire_probability.values))" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "Plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "ax = plt.axes(projection=ccrs.PlateCarree())\n", + "ax.coastlines(linewidth=1)\n", + "\n", + "fig = ax.contourf(sub_ds.longitude, sub_ds.latitude, fires,levels=levels,vmin=0,vmax=0.006,transform=ccrs.PlateCarree(),cmap=test)\n", + "\n", + "ax.add_feature(cart.feature.OCEAN,zorder=1,edgecolor='k',color='white')\n", + "ax.add_feature(cart.feature.BORDERS,linewidth=0.5,color='white')\n", + "ax.add_feature(cart.feature.LAKES,color='white', edgecolor='k', linewidth=0.2, zorder=2)\n", + "\n", + "states_provinces = cart.feature.NaturalEarthFeature(\n", + " category='cultural',\n", + " name='admin_1_states_provinces_lines',\n", + " scale='50m',\n", + " edgecolor='black'\n", + ")\n", + "\n", + "for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + "\n", + "cbar = plt.colorbar(fig,ticks=[0.0002,0.0008,0.0022,0.0044,0.0056],fraction=0.025, pad=0.04)\n", + "cbar.ax.set_yticklabels(['Low', 'Medium','High', 'Very High', 'Extreme'])\n", + "\n", + "plt.title(plot_title, fontsize=16)\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('./outputs/My_PoF_Plot.png', dpi=300)\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.12.11-01", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/handbook/PoF_Forecast.ipynb b/handbook/PoF_Forecast.ipynb deleted file mode 100755 index 3e79342..0000000 --- a/handbook/PoF_Forecast.ipynb +++ /dev/null @@ -1,205 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "455844af-4444-4882-8351-e27486fe7946", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0add9849", - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import numpy as np\n", - "import pandas as pd\n", - "import joblib\n", - "from pathlib import Path\n", - "import cftime\n", - "from xarray.coding.times import CFDatetimeCoder\n", - "import os\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9040e021", - "metadata": {}, - "outputs": [], - "source": [ - "# -----------------------------\n", - "# CONFIG\n", - "# -----------------------------\n", - "base_path = Path(\"./data/\")\n", - "year = 2003\n", - "month = 1\n", - "os.makedirs(\"./outputs/\", exist_ok=True)\n", - "output_file = f\"./outputs/POF_prediction_{year}_{month:02d}.nc\"\n", - "\n", - "# Load trained model\n", - "model = joblib.load(\"./data/POF_model.joblib\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "09d062ce", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# -----------------------------\n", - "# LOAD STATIC DATA\n", - "# -----------------------------\n", - "time_coder = CFDatetimeCoder(use_cftime=True)\n", - "PO = xr.open_dataset(base_path / \"CLIMATE/POP_2020.nc_2\")\n", - "UR = xr.open_dataset(base_path / \"CLIMATE/urban_C.nc_2\", decode_times=time_coder)\n", - "RD = xr.open_dataset(base_path / \"CLIMATE/road_density_2015_c.nc_2\")\n", - "\n", - "UR_arr = UR.vegdiff.squeeze().values # keep 2D shape\n", - "PO_arr = PO.population_density.values \n", - "RD_arr = RD.road_length.values \n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "debaf10e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Prediction saved → ./outputs/POF_prediction_2003_01.nc\n" - ] - } - ], - "source": [ - "\n", - "# -----------------------------\n", - "# LOAD DYNAMIC DATA\n", - "# -----------------------------\n", - "mon = f\"{month:02d}\"\n", - "ds_paths = {\n", - " \"AF\": f\"ACTIVE_FIRE_MAP_{year}_{mon}_R.nc\",\n", - " \"FU\": f\"FUEL_MAP_{year}_{mon}_R.nc\",\n", - " \"DF\": f\"DFMC_MAP_{year}_{mon}_R.nc\",\n", - " \"LF\": f\"LFMC_MAP_{year}_{mon}_R.nc\",\n", - " \"PR\": f\"P_{year}_{mon}.nc\",\n", - " \"T2\": f\"T2M_{year}_{mon}.nc\",\n", - " \"D2\": f\"D2M_{year}_{mon}.nc\",\n", - " \"WS\": f\"WS_{year}_{mon}.nc\",\n", - "}\n", - "# Open all dynamic datasets\n", - "with xr.open_dataset(base_path / ds_paths[\"FU\"]) as FU, \\\n", - " xr.open_dataset(base_path / ds_paths[\"DF\"]) as DF, \\\n", - " xr.open_dataset(base_path / ds_paths[\"LF\"]) as LF, \\\n", - " xr.open_dataset(base_path / ds_paths[\"PR\"]) as PR, \\\n", - " xr.open_dataset(base_path / ds_paths[\"T2\"]) as T2, \\\n", - " xr.open_dataset(base_path / ds_paths[\"D2\"]) as D2, \\\n", - " xr.open_dataset(base_path / ds_paths[\"WS\"]) as WS, \\\n", - " xr.open_dataset(base_path / ds_paths[\"AF\"]) as AF:\n", - "\n", - " n_days = len(AF.ACTIVE_FIRE)\n", - " all_grids = []\n", - "\n", - " for i in range(n_days):\n", - " # Extract arrays for timestep i\n", - " FU_LL = FU.Live_Leaf[i].values\n", - " FU_LW = FU.Live_Wood[i].values\n", - " FU_DF = FU.Dead_Foliage[i].values\n", - " FU_DW = FU.Dead_Wood[i].values\n", - " DF_ = DF.DFMC_Foliage[i].values\n", - " DW_ = DF.DFMC_Wood[i].values\n", - " LF_ = LF.LFMC[i].values\n", - " PR_ = PR.tp[i].values\n", - " T2_ = T2.t2m[i].values\n", - " D2_ = D2.d2m[i].values\n", - " WS_ = WS.ws[i].values\n", - "\n", - " # Mask where total fuel > 0\n", - " ft = FU_LL + FU_LW + FU_DF + FU_DW\n", - " mask = ft > 0\n", - "\n", - " # Flatten and build dataframe for prediction\n", - " feature_arrays = {\n", - " \"PR\": PR_[mask],\n", - " \"T2\": T2_[mask],\n", - " \"D2\": D2_[mask],\n", - " \"WS\": WS_[mask],\n", - " \"FU_LL\": FU_LL[mask],\n", - " \"FU_LW\": FU_LW[mask],\n", - " \"FU_DF\": FU_DF[mask],\n", - " \"FU_DW\": FU_DW[mask],\n", - " \"DF\": DF_[mask],\n", - " \"DW\": DW_[mask],\n", - " \"LF\": LF_[mask],\n", - " \"UR\": UR_arr[mask],\n", - " \"PO\": PO_arr[mask],\n", - " \"RD\": RD_arr[mask],\n", - " }\n", - " X_pred = pd.DataFrame(feature_arrays)\n", - "\n", - " # Predict probability\n", - " y_proba = model.predict_proba(X_pred)[:, 1]\n", - "\n", - " # Create full grid and fill masked values\n", - " fire_prob_grid = np.full(ft.shape, np.nan, dtype=float)\n", - " fire_prob_grid[mask] = y_proba\n", - " all_grids.append(fire_prob_grid)\n", - "\n", - " # Stack all timesteps into 3D array (time, lat, lon)\n", - " fire_prob_array = np.stack(all_grids, axis=0)\n", - "\n", - " # -----------------------------\n", - " # SAVE TO NETCDF\n", - " # -----------------------------\n", - " time = [cftime.DatetimeJulian(year, month, day+1) for day in range(n_days)]\n", - " ds_out = xr.Dataset(\n", - " {\"fire_probability\": ([\"time\", \"lat\", \"lon\"], fire_prob_array)},\n", - " coords={\n", - " \"time\": time,\n", - " \"latitude\": PO.latitude,\n", - " \"longitude\": PO.longitude\n", - " }\n", - " )\n", - "\n", - " ds_out.to_netcdf(output_file)\n", - " print(f\"✅ Prediction saved → {output_file}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "803a3aa7-95c8-46f2-81fd-99cb141b21c3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.12.9-01", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/handbook/PoF_Trainer.ipynb b/handbook/PoF_Trainer.ipynb deleted file mode 100755 index 952160d..0000000 --- a/handbook/PoF_Trainer.ipynb +++ /dev/null @@ -1,260 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "8233ea18", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import time\n", - "import joblib\n", - "import matplotlib.pyplot as plt\n", - "import xgboost as xgb\n", - "from xgboost import XGBClassifier, plot_importance\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, RocCurveDisplay\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fb6a66ef", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded data shape: (2162, 15)\n", - "Columns: ['AF', 'PR', 'T2', 'D2', 'WS', 'FU_LL', 'FU_LW', 'FU_DF', 'FU_DW', 'DF', 'DW', 'LF', 'UR', 'PO', 'RD']\n" - ] - } - ], - "source": [ - "\n", - "# -----------------------------\n", - "# LOAD DATA\n", - "# -----------------------------\n", - "df = pd.read_parquet(\"./data/training_data.parquet\")\n", - "\n", - "print(\"Loaded data shape:\", df.shape)\n", - "print(\"Columns:\", list(df.columns))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c69612a4", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# -----------------------------\n", - "# DEFINE FEATURES & TARGET\n", - "# -----------------------------\n", - "target_col = \"AF\"\n", - "feature_cols = [c for c in df.columns if c not in [\"AF\"]]\n", - "\n", - "X = df[feature_cols]\n", - "y = df[target_col]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "32260560", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# -----------------------------\n", - "# TRAIN / TEST SPLIT\n", - "# -----------------------------\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.2, random_state=42, stratify=y\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "90c00182", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training XGBoost Binary Classifier...\n", - "✅ Training completed in 0.18 seconds\n" - ] - } - ], - "source": [ - "\n", - "# -----------------------------\n", - "# TRAIN CLASSIFIER\n", - "# -----------------------------\n", - "print(\"Training XGBoost Binary Classifier...\")\n", - "start_time = time.time()\n", - "\n", - "model = XGBClassifier(\n", - " objective=\"binary:logistic\",\n", - " tree_method=\"hist\",\n", - " n_estimators=300,\n", - " max_depth=8,\n", - " learning_rate=0.1,\n", - "# subsample=0.8,\n", - "# colsample_bytree=0.8,\n", - " eval_metric=\"logloss\",\n", - "# random_state=42,\n", - " n_jobs=-1\n", - ")\n", - "\n", - "model.fit(X_train, y_train)\n", - "\n", - "end_time = time.time()\n", - "print(f\"✅ Training completed in {end_time - start_time:.2f} seconds\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4c19db4a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Classification report:\n", - " precision recall f1-score support\n", - "\n", - " 0.0 0.993 0.998 0.995 430\n", - " 1.0 0.000 0.000 0.000 3\n", - "\n", - " accuracy 0.991 433\n", - " macro avg 0.497 0.499 0.498 433\n", - "weighted avg 0.986 0.991 0.988 433\n", - "\n", - "ROC-AUC: 0.967\n", - "\n", - "Confusion Matrix:\n", - "[[429 1]\n", - " [ 3 0]]\n" - ] - } - ], - "source": [ - "\n", - "# -----------------------------\n", - "# EVALUATE MODEL\n", - "# -----------------------------\n", - "y_pred = model.predict(X_test)\n", - "y_proba = model.predict_proba(X_test)[:, 1]\n", - "\n", - "print(\"\\nClassification report:\")\n", - "print(classification_report(y_test, y_pred, digits=3))\n", - "\n", - "roc_auc = roc_auc_score(y_test, y_proba)\n", - "print(f\"ROC-AUC: {roc_auc:.3f}\")\n", - "\n", - "# Confusion matrix\n", - "print(\"\\nConfusion Matrix:\")\n", - "print(confusion_matrix(y_test, y_pred))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "66d0964f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
CC BY-NC-SA
diff --git a/handbook/_config.yml b/handbook/_config.yml deleted file mode 100644 index 920860d..0000000 --- a/handbook/_config.yml +++ /dev/null @@ -1,49 +0,0 @@ - -# Book settings -# Learn more at https://jupyterbook.org/customize/config.html - -title: "POF IN A BOX" -author: Christopher Barnard -logo: ../images/image.png - -# Force re-execution of notebooks on each build. -# See https://jupyterbook.org/content/execute.html -execute: - execute_notebooks: force - -# Define the name of the latex output file for PDF builds -latex: - latex_documents: - targetname: book.tex - -# Add a bibtex file so that we can create citations -# bibtex_bibfiles: -# - references.bib - -# Information about where the book exists on the web -repository: - url: https://github.com/enyfeo/POF_IN_A_BOX/handbook # Online location of your book - path_to_book: docs # Optional path to your book, relative to the repository root - branch: develop # Which branch of the repository should be used when creating links (optional) - -# Add GitHub buttons to your book -# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository -# https://jupyterbook.org/en/stable/advanced/html.html -html: - use_issues_button: true - use_repository_button: true - -sphinx: - config: - html_css_files: ["custom.css"] - html_show_copyright: false - language: en - html_js_files: - - https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js - suppress_warnings: ["mystnb.unknown_mime_type"] - -execute: - timeout: 500 - -exclude_patterns: - - handbook/data \ No newline at end of file diff --git a/handbook/_site/site.yml b/handbook/_site/site.yml new file mode 100644 index 0000000..b4933f7 --- /dev/null +++ b/handbook/_site/site.yml @@ -0,0 +1,23 @@ +--- +version: 1 +site: + template: book-theme + nav: + - title: Data Portals + children: + - title: Early Warning Data Store (EWDS) + url: https://ewds.climate.copernicus.eu + - title: Cross Data Store (XDS) + url: https://xds-preprod.ecmwf.int + + # - title: Contribute + # url: /contribute + # Commenting this out while there's a bug that adds BASE_URL to the nav links + # - title: Blog + # url: https://blog.jupyterbook.org + - title: Help + children: + - title: Ask a question ❓ + url: https://confluence.ecmwf.int/site/support + - title: Report a bug 🐛 + url: https://github.com/ecmwf-projects/AI-Probability-of-Fire/issues diff --git a/handbook/_toc.yml b/handbook/_toc.yml deleted file mode 100755 index 882e914..0000000 --- a/handbook/_toc.yml +++ /dev/null @@ -1,18 +0,0 @@ ---- -# Table of contents -# Learn more at https://jupyterbook.org/customize/toc.html - -format: jb-book -root: index - -parts: - - caption: "Retrieving data" - chapters: - - file: POF_Data_Generator - title: "POF Data Generator" - - caption: "Training and forecasting" - chapters: - - file: POF_Trainer - title: "POF Trainer" - - file: POF_Forecast - title: "POF Forecast" \ No newline at end of file diff --git a/handbook/data/ACTIVE_FIRE_MAP_2003_01_R.nc 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This type of prediction is often referred to as an **analysis prediction**, because it relies on observation-based data. An alternative—mirroring how PoF is used operationally at ECMWF—is to use *forecast* inputs. These are typically short-range (up to ~10 days), but the same workflow can be extended to seasonal, annual, or even decadal predictions. + +--- + +
+ Loading Data
+
+
+We begin by loading the previously trained model **POF_model.joblib**.
+
+As with the training workflow, you must then load the required input data:
+
+- **Static data** (e.g., population, roads), which do not change with time.
+- **Time-varying data** (e.g., meteorological inputs, vegetation indices).
+ This section also demonstrates how to load active fire data, which can be useful for evaluation.
+
+---
+
+
+ Running the Prediction
+
+
+After constructing a dataframe that matches the structure used during training, you can run the model in probability mode to estimate the likelihood of fire occurrence (`1`) using: model.predict_proba(X_pred)
+
+
+---
+
+
+ Saving Prediction as NetCDF
+
+
+The final step is to save your prediction as a NetCDF file for further analysis or visualisation.
+In this example, metadata is not included, but you can add variable attributes, CRS information, or global metadata as required.
+
+Your output file should be named:
+
+**POF_prediction_YYYY_MM.nc**
\ No newline at end of file
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--- a/handbook/index.md
+++ b/handbook/index.md
@@ -1,40 +1,56 @@
-
-
-
+
+
+:::{figure} ./images/PoF-in-a-box.png
+:align: center
+:width: 70%
+PoF in a BoX
+:::
__PoF in a Box provides a ready-to-run set of notebooks designed to help you get started with your own Predictability of Fire (PoF) experiments. It includes all the tools, data access, and workflows needed to explore fire activity prediction, from input preparation to model evaluation—right out of the box.__
-+ 🔥 Probability of Fire — In a Box +
++ Reproducible notebooks for building local PoF models with your own data +
+ Before you begin: Please note that the workflow presented here is not intended to reproduce the exact operational system currently running at ECMWF. Instead, it provides a simplified sequence of steps designed to help you get started.
-The notebook can only be used with past forecasts—it will generate a tool that allows you to explore and analyse previous forecast data.
-That said, we believe this provides a valuable starting point: a complete, working system built entirely from data that are publicly available through the Climate Data Store (CDS) or accessible via temporary data repositories linked within this notebook.
-
+:::{note} Before you begin: Please note that the workflow presented here is not intended to reproduce the exact operational system currently running at ECMWF. Instead, it provides a simplified sequence of steps designed to help you get started.
+:::
+The notebook can only be used with past forecasts—it will generate a tool that allows you to explore and analyse previous forecast data.
+That said, we believe this provides a valuable starting point: a complete, working system built entirely from data that are publicly available through the Climate Data Store (CDS) or accessible via temporary data repositories linked within this notebook.
-# Wildfire prediction
-Given their chaotic nature, how can we know when and where wildfires will occur? That is a question that has puzzled wildfire forecasters for decades and now, thanks to advances in **machine learning**, we are one step closer to answering it, although we still have some way to go.
+## Wildfire prediction
+Given their chaotic nature, how can we know when and where wildfires will occur? That is a question that has puzzled wildfire forecasters for decades and now, thanks to advances in **machine learning**, we are one step closer to answering it, although we still have some way to go.
+
The topic of **wildfire forecasting** is not a new one. However, in recent years media attention around the subject has grown as unprecedented wildfire seasons in Australia (2019/2020) and Canada (2023) and Europe (2025) have resulted in widespread devastation of local ecosystems and communities.
These events have far-reaching consequences for both air quality and greenhouse gas emissions
## Traditional fire forecasting
-For nearly half a century, fire danger forecasts have relied on a method that links weather conditions with fire activity to create an index of fire risk, with the **Canadian Fire Weather Index (FWI)**
+For nearly half a century, fire danger forecasts have relied on a method that links weather conditions with fire activity to create an index of fire risk, with the **Canadian Fire Weather Index (FWI)**
being the most widely used. However, this approach has its limitations.
-
-
-
-
-
+ Preparing Data
+
+
+You can read in your netcdf and define a lat/lon bounding box to plot your data.
+
+You can then create a colour pallete and set limits to your data so that it falls within defined levels for visualisation.
+
+
+ Plotting Data
+
+
+The example plots the data using equirectangular projection but alternatives can be used. The plotting routine also adds various map features and colourbars which can be adapted.
+
+
+ Saving Figure
+
+
+The plot can then be saved in the format of your choice, the example produces a png.
\ No newline at end of file
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+++ b/handbook/retrieving_data.md
@@ -0,0 +1,106 @@
+# The data behind PoF
+
+
+__A data driven model needs data__
+
+:::{note}
+You will not be able to reproduce the exact configuration run at ECMWF, as it uses some proprietary data that we are unable to share. However, most of the data used to train PoF are publicly available and free to use. Below, we provide guidance on the datasets we recommend for getting started on your PoF journey.
+:::
+
+:::{figure} ./images/data-sources-pof.png
+:align: center
+:width: 70%
+Data sources to train the PoF
+:::
+
+
+ Weather Data
+
+
+
+In fuel-rich environments, weather is the dominant control of fire behaviour.
+
+To begin your data collection, we recommend focusing on four key environmental variables: __temperature, precipitation, relative humidity, and wind speed.__ These variables represent the fundamental controls of how fire behaviour, and they are at the core of nearly all fire-weather indices used operationally around the world.
+
+Each variable plays a distinct role in shaping landscape flammability. Temperature is one of the most immediate drivers: as temperatures rise, vegetation and soils lose moisture more quickly, and both live and dead fuels become more prone to ignition. Prolonged warm conditions can push even healthy vegetation into moisture stress, making it more combustible.
+
+__Precipitation__ acts in the opposite direction. It replenishes moisture in fuels and slows the drying process, reducing the likelihood that a spark will result in a fire. But precipitation also shapes fire risk over longer periods. Wet springs or wet years stimulate vegetation growth, building up large fuel loads which can later burn if drought conditions return.
+
+__Relative humidity__ is closely linked to fuel moisture, particularly for fine fuels like grasses and leaf litter. When the air is dry, these fuels lose moisture quickly and ignite much more easily. When humidity is higher, even small increases in atmospheric moisture can keep fine fuels from reaching the critical dryness needed to sustain fire spread. Because humidity changes rapidly over the course of a day, it introduces fast variations in fire danger. The data used here derives relative humidity by first computing the saturation vapor pressure at the air temperature and the actual vapor pressure at the dew point (both via a Clausius–Clapeyron/Magnus approximation), then taking the ratio of actual to saturation vapor pressure multiplied by 100.
+
+Finally, __wind speed__ is the most dynamic and often the most dangerous factor. Wind supplies oxygen to flames, drives heat into unburnt areas, and can carry embers far ahead of the main fire front, creating spot fires and accelerating spread. Strong winds can transform a small ignition into a fast-moving, intense wildfire within minutes.
+
+Together, these __four variables__ describe the conditions under which fires can ignite and spread. They provide a solid, intuitive foundation for understanding landscape flammability and are a suitable starting point for anyone beginning their PoF modelling journey. They also provide a data-driven alternative to the Fire Weather Index.
+
+---
+
+
+ Fuel Data
+
+
+
+In absence of any other source, fuel is the most important control globally on fire activity.
+
+To model how vegetation influences wildfire behaviour, two key aspects must be captured, how much fuel is present and how dry that fuel is.
+
+ __Fuel load__ represents the total mass of above-ground biomass available to burn. It includes:
+
+🌿 Foliage (live and dead)
+🌳 Wood (live and dead)
+
+We estimate fuel load by combining Satellite-derived Above-Ground Biomass (AGB) from ESA-CCI (2010 baseline), and Daily Net Ecosystem Exchange (NEE) from the ECLand land-surface model (forced by ERA5) with bias correction from atmospheric inversions.
+
+This approach allows us to reconstruct daily biomass evolution at ~9 km resolution. AGB is then partitioned into live and dead components using vegetation-type-specific ratios and leaf area index. This gives a dynamic estimate of foliage and wood fuel loads, consistent with fire-modelling practices.
+
+ __Live Fuel Moisture Content (LFMC)__
+
+💦🍃 LFMC expresses how much water is contained in living vegetation relative to its dry mass. It determines how easily plants ignite and how fast a fire can spread.
+We use a semi-empirical model, trained on the Globe-LFMC in-situ dataset, to estimate daily LFMC from:
+
+🌿 **Leaf Area Index (LAI)**
+
+💧 **Soil moisture (root-weighted)**
+
+🌳 **Vegetation type**
+
+The model ensures physically realistic moisture ranges and captures seasonal vegetation responses to drought and growth cycles.
+
+ __Dead Fuel Moisture Content (DFMC)__
+
+DFMC describes the moisture content of dead leaves, litter, and woody debris—fuels that respond directly to weather.
+We generalize the Nelson (2000) physical model to estimate DFMC for standard dead-fuel classes (1h, 10h, 100h, 1000h). Short-lag fuels (1h, 10h), which represent dead foliage, respond to fast humidity changes. Long-lag fuels (100h, 1000h), which represent dead wood, respond to multi-day weather patterns. DFMC for foliage and wood is derived by weighting appropriate fuel classes based on vegetation type.
+
+---
+
+
+ Sources of Ignitions
+
+
+**Road Density**
+
+Roads facilitate human access to wildland areas, increasing the likelihood of ignitions through activities such as agriculture, forestry, and recreation. However, they may also contribute to fire suppression efficiency and serve as firebreaks, creating a complex relationship between road density and fire outcomes.
+
+The Global Roads Inventory Project (GRIP) dataset, which provides a global map of road networks at ~8 km resolution, has been regridded for use here.
+
+**Population Density**
+
+Around 90% of wildfires are human‑ignited, making human presence a useful indicator of potential fire occurrence. Fire incidence generally rises with population density but eventually saturates and at very high densities, suppression efforts typically limit the potential for large wildfires.
+
+The data provided here are derived from the Gridded Population of the World, Version 4 (GPWv4) dataset, which provides gridded population estimates at ~1 km resolution in 5-year intervals from 2000 to 2020.
+
+---
+
+
+ Fire Data
+
+
+Various sensors and fire products are available for use in a PoF-style system. Here we attempt a binary classifier model, that is an indication of the probability of a fire yes/no. As such we use hotspot detections rather than burned area.
+
+Active fire (AF) detections were taken from the MODIS MCD14ML product, which provides daily fire hotspot locations based on thermal anomalies detected at 1 km resolution. These data were gridded to coarser daily resolution and represented as binary values (1 = at least one hotspot detected, 0 = none). We applied quality assurance flags to exclude low-confidence detections and removed spurious signals when possible.
+
+:::{important}
+ 🎯 Final result
++ A set of environmental predictors and fire activity collocated dataset +
+ \ No newline at end of file diff --git a/handbook/training.md b/handbook/training.md new file mode 100644 index 0000000..d522cfc --- /dev/null +++ b/handbook/training.md @@ -0,0 +1,89 @@ +# How we train an XGBoost Model for PoF + +PoF uses XGBoost, a powerful gradient-boosted decision-tree algorithm widely applied to tabular environmental data. + +The training procedure follows a simple, reproducible workflow based on a probabilistic classifier: + +__Prepare the training dataset__ + +You will assemble a table where each row represents a gridcell in space and time (daily & 9km grid) and includes: + +--- + +
+ Predictors (features)
+ Fuel variables, meteorological variables, and ignition proxies. All of which are described in the retrieving_data documentation. +
+
+---
+
++ Fuel variables, meteorological variables, and ignition proxies. All of which are described in the retrieving_data documentation. +
+ Target (label)
+ Binary fire occurrence within the gridcell on the given day, where a single or multiple counts equate to fire detection:
+
+The data generation script should have synthesised your data into a DataFrame stored in a Parquet file, which is now ready for training.
+
+__Split the data__
+
+---
+
++ Binary fire occurrence within the gridcell on the given day, where a single or multiple counts equate to fire detection:
1 = fire detected, 0 = no fire.
+
+ Dataset Splits
+
+
+We typically use a random stratified split.
+
+__Define the XGBoost model__
+
+We configure the key parameters:
+
+---
+
++
-
+
- Training set → used to fit the model +
- Test set → final skill evaluation +
+ XGBoost Hyperparameters
+
+
+You will need to adjust these parameters depending on your region and data volume.
+
+__Generate PoF predictions__
+
+Once trained, the model outputs a probability between 0 and 1 representing the likelihood that at least one fire will occur under the given conditions withing a gridcell on a given day.
+
+
+These are the core PoF predictions you will visualise and evaluate.
+
+
+__Evaluate the model__
+
+We can assess the model skill using tools such as:
+ROC curve
+AUC score
+Reliability diagrams
+Confusion matrix
+
+However we only provide examples for a subset of these metrics. Reliability methods are highly recommended given the unbalanced nature of PoF, however it is important to note these can be more computationally expensive than some other methods.
+
+Interpretation of these diagnostics in the context of fire risk needs to be carefully considered.
+
+The script saves the trained model for reuse (POF_model.joblib) . Later versions of XGboost may save as json or other file formats but can be saved and reused in the same or similar way.
+
+---
+
++
-
+
- max_depth – tree complexity +
- learning_rate – how fast the model learns +
- n_estimators – number of boosting rounds +
- objective="binary:logistic" – required to output probabilities +
+
\ No newline at end of file
diff --git a/myst.yml b/myst.yml
deleted file mode 100644
index 737025d..0000000
--- a/myst.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-# See docs at: https://mystmd.org/guide/frontmatter
-version: 1
-project:
- id: ca4d97b9-09e7-4ed5-9230-9dee58e5201c
- # title:
- # description:
- # keywords: []
- # authors: []
- github: https://github.com/ecmwf-projects/AI-Probability-of-Fire
- # To autogenerate a Table of Contents, run "jupyter book init --write-toc"
-site:
- template: book-theme
- # options:
- # favicon: favicon.ico
- # logo: site_logo.png
🎯 Final result
++ A trained, validated XGBoost model providing daily probability-of-fire estimates + from environmental predictors +
+