Empirical Mode Decomposition Couple with Artificial Neural Network for Water Level Prediction

Abstract

Natural disaster brings massive destruction towards properties and human being and flood is one of them. In order for the government to take earlier action to reduce the damages, an accurate flood prediction is necessary. In Malaysia, Kelantan is categorized as a high flood risk area, thus this study focuses on Kelantan flood prediction. This study is to investigate the effect of decomposition for water level prediction by applying Artificial Neural Network (ANN) forecasting model. In this study, Empirical Mode Decomposition (EMD) is used as the decomposition method. The best Intrinsic Mode Function (IMF) for each input variable is selected using correlation-based selection method. The results showed that the performance of hybrid EMD and ANN is superior compared to other models, especially classic ANN model. The reason for this outcome is that through decomposition methods, ANN is able to capture more in-depth information of the Kelantan hydrological time series data. The resulting model provides new insights for government and hydrologist in Kelantan to have better prediction towards flood occurrence.