Spatio-Temporal Agnostic Deep Learning Modeling of Forest Fire Prediction Using Weather Data

Abstract

This research provides a spatio-temporal agnostic framework based on subsampling to generate generic deep learning models using publicly available weather data and to predict the probability of forest fire and severity. The aim is to show that this framework can be used to subsample and generate a balanced dataset for generic deep learning models to improve predictions for forest fires. The framework works for binary classification and regression deep learning models. It also works with limited variations between fire and non-fire data. Using this framework, 45 of the binary classification models built produced an F1Score greater than 0.95 while 35 of 54 regression models produced an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Score greater than 0.91.