A neural network approach for predicting forest fires

In this paper, we present an application of artificial neural networks to the real-world problem of predicting forest fire. The neural network used for this application is a multilayer perceptron whose architectural parameters, i.e., the number of hidden layers and the number of neurons per layer were heuristically determined. The synaptic weights of this architecture were adjusted using the backpropagation learning algorithm and a large set of real data related to the studied problem. We also present and discuss some preliminary results which illustrate the performance and the usefulness of the proposed approach.