Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm

Abstract

The Department of Irrigation and Drainage (DID) and Meteorological Malaysia Department (MMD) have identified that water level is one of the important indicators for flooding control. The aim of this study is to find the best regression model and to identify the dominant variables of water level in Dungun River. Autoregressive Integrated Moving Average (ARIMA),Seasonal ARIMA (SARIMA), Backpropagation Neural Network (BPNN) and Nonlinear Autoregressive Exogenous Model (NARX) are popular methods in time series forecasting. However, ARIMA and SARIMA produce linear models where the approximations of linear models for the complex real-world problems are not always satisfactory. Thus, Backpropagation Neural Network (BPNN) and Nonlinear Autoregressive Exogenous Model (NARX) can be implemented in the time series forescasting due to its nonlinear modelling capability. These four methods, however, cannot be used directly for water level prediction since the original data from DID and MMD contain missing data. In this thesis, two methods are employed to treat missing data which are pre-processing using Mean and preprocessing using Ordinary Linear Regression (OLR) substitutions. In addition, BPNN and NARX may be difficult to determine the optimal network architecture and weights design since the optimal weight are different in each learning process. Thus, it is difficult to get best model in prediction. Based on the limitation of BPNN and NARX, the hybridization of Single BPPN and Genetic Algorithms (S-BPNN-GA) and Multi BPNN and Genetic Algorithms (M-BPNN-GA) have been proposed in this study. Experiments indicate hybridization of M-BPNN-GA 5-6-1 using five predictor variables including monthly, rainfall, temperature, evaporation and humidity and give better results compared to the other methods.