Mutli-Modality Federate Learning with Multi-Source Data for Forest Fire Prediction

Abstract

Forest fires result in significant destruction of natural resources and human lives.They are commonly caused by humans or naturally occurring phenomena like lightning strikes. They may start due to lightning when necessary climatic conditions prevail for a the fire to ignite. The available forest fire data contains both fire and non-fire data and is highly imbalanced. To overcome this, the thesis provides a spatio-temporal agnostic subsampling (STAS) framework to subsample the highly imbalanced forest fire data. The proposed framework also works with limited variations between fire and non-fire data. Apart from lightning, weather and drought conditions are also significant factors in forest fires. Forest fire prediction which does not take these into consideration, may not be accurate. Therefore, the thesis is also focused on building and applying a multi-modality modeling approach for predicting forest fires. The multi-modality model is built using available datasets on forest fires, lightning, weather, and hydrometrics to predict the probability of forest fires. Using the proposed STAS framework and multi-modality model, the research aimed to generate a generalized model by applying multi-modality federated learning to predict forest fires independent of geographic location without the need for calibration.This research was able to successfully work with federated learning framework and multi-modality model to obtain high F 1Score (> 0.9) and R 2 Score (> 0.8).