La survenue des catastrophes naturelles : classification des variables explicatives par les réseaux de neurones

Abstract

In recent decades, the occurrence of natural disasters has been sharply increasing. These disastrous risks hit different countries in many dispersed areas and will most likely continue to be real threats in the world. Since no country is immune to natural disasters. It therefore seemed appropriate to test the determinants of their occurrence particularly with the restriction in their period of return and their increasing chance of occurrence. We booked this paper to test the factors underlying the occurrence of natural disasters. Our work is based on the application of an artificial neural network that is the multilayer perceptron. We use this network to predict the number of natural disasters from the variables the most mentioned by literature. Then, we applied the sensitivity analysis for classifying explanatory variables according to their influence on the natural disasters number during the period of 1990-2007.
The obtained results showed that our neural network predicts well the number of natural disasters. Indeed, all the explanatory variables have a significant effect on the neural output. So, they contribute considerably in the explanation of a complex problem such the occurrence of natural disasters.