Flood Prediction using Artificial Neural Networks: Empirical Evidence from Mauritius as a Case Study

A. Zaynah Dhunny, Reena Hansa Seebocus, Zaheer Allam, Mohammad Yasser Chuttur, Muhammed Eltahan, Harsh Mehta


Artificial Neural Networks (ANN) has been well studied for flood prediction. However, there is not enough empirical evidence to generalize ANN applicability to small countries with microclimates prevailing in a small geographical space. In this paper, we focus on the climatic conditions of Mauritius for which we seek to investigate the accuracy of using ANN to predict flooding using locally collected data from 11 meteorological stations spread across the country. The ANN model for flood prediction presented in this work is trained using 20,000 climate data records, collected over a period of two years for Mauritius. Our input climate features are minimum temperature, maximum temperature, rainfall and humidity and our output decision is „flood‟ or „no flood‟. Using ANN, we achieved an accuracy of 98% for flood prediction and hence, we conclude that ANN is indeed a good predictor for flood occurrence even for regions with predominantly microclimatic conditions.

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DOI: http://dx.doi.org/10.17977/um018v3i12020p1-10


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