Enhancing Daily Wildfire Risk Prediction Application Through Interpretable Machine Learning Results

Alexis Apostolakis, Stella Girtsou, Konstantinos Alexis, Giorgos Giannopoulos, Nikolaos S. Bartsotas, Charalampos Kontoes

Abstract: Over the last decade, the use of Machine/Deep learning algorithms and methodologies has found widespread application across various domains in wildfire science, with fire occurrence risk prediction being one of the extensively covered areas. Many algorithms and architectures have been explored to solve the problem as a binary classification task focusing mainly on the model’s classification performance through the standard classification metrics. However, in the context of an application for predicting the fire occurrence risk we have to enhance the trust in the model’s output by deriving a well defined fire susceptibility index, as well as interpretable insights for the model’s predictions when applied to real-world datasets. In this manuscript we propose model agnostic solutions towards (a) the production of a probability-based binning of the model’s prediction distribution for generating a scalar wildfire occurrence risk and (b) of a representative sampling for producing local prediction explanations spanning the whole dataset, to tackle the slow processing times of explainable AI frameworks, when generating vast amounts of local interpretations. The objective is to contribute the development of a reliable application predicting next day’s fire risk, facilitating the adoption of ML-based solutions by users who are not experts in data science.

External IDs:dblp:conf/igarss/ApostolakisGAGBK24