Application of Artificial Neural Network in Wildfire Early Prediction Systems

Abstract

The preservation of forest ecosystems is of vital importance to life on our planet. The increased losses of forests due to fires make the task of forest fire prevention of crucial significance. The present paper describes the development of an artificial neural network (ANN) for forest fire early prediction. The ANN predictor consists of two layers with 5 neurons in the hidden layer. It is trained through backpropagation of an error learning algorithm and is validated to provide prediction with a high degree of accuracy. An additional advantage of the designed predictor is the use of a limited number of input data based on weather and moisture conditions and of an output of a prior computed probability for fire. The training and validation datasets consist of 82 records of real measurement data. The developed and validated ANN can contribute to improvement of the current forest fire prediction systems.