An evolutionary Multilayer Perceptron algorithm for real time river flood prediction

Severe flash flood events give very little opportunity for issuing warnings. In this paper, we approach the automated and real time prediction of river flooding by proposing and evaluating different variations of the conventional Multilayer Perceptron (MLP) machine learning algorithm. Our first approach follows a trial and error attempt to optimize the MLP architecture. The second and third approaches are based on the application of nature inspired evolutionary techniques, namely the Genetic Algorithm (MLP-GA) and the Bat Algorithm (MLP-BA) respectively. The MLP-GA generates an improved MLP configuration and MLP-BA enhances the training method. Our fourth, novel approach (MLP-BA-GA) is based on the application of GA to further optimize both the BA and MLP architecture. When compared with previous work, experiments show improvement in the accuracy of river flood prediction, with significant results for the MLP-BA-GA.

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