California Wildfire Prediction using Machine Learning

Abstract

Wildfires in California are increasingly frequent and caused a vast range of damages to the land and society. Existing work in the field of machine learning aided fire susceptibility mapping contains promising results, however they focused on small fire prone areas or yielded less than impressive classification metrics, with even fewer groups focusing on California. In this paper, we applied five machine learning models for fire prediction using a series of remote sensing data and fire incident records of California. The fire incident records were used for labeling fire conditions, and remote sensing features (land surface temperature, normalized difference vegetation index, and thermal anomalies) were selected for model training. Two datasets were created for analysis of wildfires in California based on different sized regions: the whole state treated as one region, and California being broken into counties. For the state level dataset, fire prediction accuracies were 89% for artificial neural network (ANN), and 87% for Gaussian Naive Bayes (GaussianNB), k-nearest neighbors (KNN), and logistic regression (LR), and support vector machine (SVM). For the county level dataset, the prediction accuracies were 97% for KNN, 96% for ANN, SVM, and LR, and 94% for GuassianNB.