Impact wave predictions by a Fuzzy ARTMAP neural network

Abstract

Impact waves caused by landslides, rock blocks, and avalanches in lakes and dam reservoirs have provoked several disasters since the dawn of time; further, they have caused considerable number of deaths and property loss. Understanding the behavior and characteristics of these waves, principally their height, propagation velocity, and energy, are key for determining engineering parameters, and therefore developing alert systems and evacuation plans. Several studies have investigated impact waves using experimental, mathematical, and numerical models; however, few have used artificial neural networks. Considering the learning and prediction characteristics of neural networks, this work explores their application in the prediction of impact waves. The main objective is to verify the capability of a fuzzy ARTMAP neural network in predicting the evolution of the maximum wave height, one of the main parameters of impact waves. The experimental data used in this paper are from the work developed by Huber (1980) when studying the impact waves generated by dropping deformable material (granular) in a channel. This study uses different forms of normalization, as well as different training parameters. The Fuzzy ARTMAP neural network predicts adequately the evolution of the maximum wave heights, becoming useful for this kind of application.