A Machine Learning Methodology for Next Day Wildfire Prediction

In this paper, we handle the problem of next day wildfire prediction via the use of machine learning. In contrast to most works in the relevant literature, we set the problem to its realistic basis, with respect to its large scale, the extreme imbalance in the data distribution, the required high spatial granularity of the predictions and the consideration of the strong spatial correlations inherent in the data. We implement a machine learning workflow that exploits Tree Ensemble and Neural Network algorithms, upon which an extensive hyperparameter search procedure is performed, via cross-validation, in order to select a set of effective models that are expected to generalize well on new data. Our experiments on the whole Greek territory demonstrate the effectiveness of the proposed methodology, rendering it directly applicable to real-world scenarios. Finally, several insights towards further improving the effectiveness of current models are discussed.