Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction

Wildfires play a pivotal role in environmental processes and the sustainable development of ecosystems. Timely responses can significantly reduce the damages and consequences caused by their spread. Several critical issues in wildfire behavior analysis include fire occurrence forecasting, early detection, and spread prediction. In this study, we focus on wildfire occurrence forecasting, which is a valuable tool for facilitating earlier intervention. Conventional approaches primarily rely on the computation of fire indices based on weather conditions. However, solutions that utilize more comprehensive environmental data, remote sensing information, and artificial intelligence (AI) algorithms may offer substantial advantages for rapid decision-making and extensive territory monitoring. The wide variety of spatial environmental parameters and the great diversity of geographical regions that influence wildfire occurrence complicate this task. Consequently, there is no unified approach for predicting wildfire occurrences using remote sensing data and AI techniques. The goal of this study is to explore the potential of predicting wildfire occurrences using various available environmental parameters - meteorological, geo-spatial, and anthropogenic - and machine learning (ML) algorithms. We developed a unified pipeline for data acquisition and subsequent ML-based algorithm development. The comprehensive analysis includes the following algorithms: Random Forest, XGBoost, Autoencoder, ConvLSTM, Attention Multilayer Perceptron, and RegNetX. In addition, we explore several metrics to assess the quality of developed models in case of highly imbalanced spatio-temporal data. To conduct the study, we collected a unique dataset covering several large regions in central Russia, incorporating more than 17,000 verified wildfire events over a period of 10 years. The findings underscore the necessity of developing individual ML models tailored to each region, taking into account the specific environmental features correlated with the probability of fire occurrence. The quality of the achieved models, as measured by F1-score, varies from 0.7 to 0.87 depending on the region, demonstrating the potential of integrating such algorithms into emergency response systems.