Wavelet decomposition-NNARX model for flood prediction of Kelantan River, Malaysia

Flood is a major disaster that happens around the world. It has caused the loss of many precious lives and massive destruction of property. The possibility of flood can be determined depends on many factors that consist of rainfall, structure of the river, flow rate of the river etc. One of the research challenges is to develop accurate prediction models and what improvement can be made to the forecasting model. The objective of this thesis is to improve the performance of the neural network model to predict the flood on the Kelantan River, Malaysia. A technique for modelling of nonlinear data of flood forecasting using wavelet decomposition-neural network autoregressive exogenous input (NNARX) approach is proposed. This thesis discusses the identification of parameters that involved in the forecasting field as rainfall value, flow rate of the river and the river water level. With the original data acquired, the data had been processing through to wavelet decomposition and filtered to generate a new set of input data for NNARX prediction model. This proposed technique has been compared with the non-wavelet NNARX. The experimental result show that the proposed approach provides better testing performance compared to its counterpart, which the mean square error obtained is 2.0491e⁻⁴ while the normal NNARX is 6.1642e⁻⁴.

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