Application of Discrete Wavelet Transformation with Artificial Neural Network in Kelantan Water Level Prediction

Eng Chuen Loh, Shuhaida binti Ismail, Aida Mustafa, Shazlyn Milleanna Shaharudin

Flood is one of the most destructive natural disaster that occurred commonly accross the globe. For decade, human being suffers from it, result in loss of properties and even lifes. In Peninsular Malaysia, Kelantan is categorized as a flood proned area, thus this study focus on Kelantan flood prediction. The aim of this study is to investigate the effect of decomposition and the suitable model for Kelantan river’s water level prediction by applying Artificial Neural Network (ANN) forecasting model. The decomposition method used is Discrete Wavelet Transformation (DWT) and there are four different types of family wavelet with 3 different order are used in this study which are haar, sym2, sym3, db2, db3, coif1, coif2, and coif3. The results showed that the performance of hybrid DWT with family wavelet of 3rd Symlets Wavelet and ANN is superior compared to other models, especially classic ANN model. The reason for this outcome is that through decomposition methods, ANN 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.

Volume 11 | 12-Special Issue

Pages: 82-88