FireRisk: A Web Platform for next day fire forecasting

Introduction. This abstract presents FireRisk (https://riskmap.beyond-eocenter.eu/), a web platform that produces and visualizes timely, highly granular and accurate next day fire risk predictions on a country scale. FireRisk deploys a thorough data fusion process, and a state of the art machine learning (ML) pipeline, considering a large set of fire driving factors, in order to train scalable and accurate models for next day fire prediction. On top of them, it implements a web service that supports the visualization of fire risk predictions and metadata on a user friendly, map-based web application. 

The FireRisk platform. The high-level architecture is depicted in the following figure. It comprises three major components.

(a) Data fusion: This component implements the collection, preprocessing, curation and harmonization of data, leading to the generation of a rich feature set of factors that affect fire occurrence and spread. 25 fire influencing factors were considered, including topography-related, meteorology-related, Earth Observation (EO) derived variables, and historical fire occurrence information. These have been extensively documented in [1].

(b) ML model learning: This component implements a complete ML pipeline, that includes training and comparison of various ML algorithms, hyperparameter tuning and model (cross-)validation and selection. This pipeline allows the configurable production of robust ML models for fire risk prediction. It is extensively documented in [2].

(c) Web platform: This component provides an interactive daily fire risk map to users through a web interface. The user is able to view the next day fire risk predictions for the current, as well as for historical days. The predictions are depicted in a five-grade scale (from very low to very high) adopting a five-grade coloring (blue to red). The user is also able to seamlessly change the zoom level, from the whole country level, to individual fine grained areas (grid cells 500m wide), for which individual predictions are provided. Finally, the web interface can be displayed on mobile devices, where the user can additionally view their position on the map.

The risk map visualization functionality is implemented through a Web Map Service (WMS) that is configured on a GeoServer back-end installation. The daily map is stored in PostgreSQL as a raster image, using the geospatial extension PostGIS. For implementing we engage the WMS GeoServer’s capability to convert PostGIS geospatial tables to WMS.

Ongoing work. Our ongoing work focuses on two directions: (a) We are adapting Deep Learning algorithms (Siamese Neural Networks and Semantic Segmentation CNNS), to better handle the extreme imbalance and the strong spatio-temporal correlations in the data. (b) We are incorporating explainability mechanisms that will allow the end user of the web application to receive simple and intuitive explanations on each individual prediction visualized on the map, based on the underlying fire driving factors.

1. Girtsou, S. et al.. A Machine Learning methodology for next day wildfire prediction. In IGARSS, 2021.

2. Apostolakis, A.; et al. Estimating Next Day’s Forest Fire Risk via a Complete Machine Learning Methodology. In Remote Sens. 2022. https://doi.org/10.3390/rs14051222

Acknowledgement: Co-funded by Greece and the European Union through the Regional Operational Programme of Attiki, under the call "Research and Innovation Synergies in the Region of Attica” (Project code: ΑΤΤΡ4-0340489).