Modelling of Uncertain System: A comparison study of Linear and Non-Linear Approaches

Abstract

Modelling of a river involves protracted engagement with uncertainty, thus making developing a reliable model becomes quite cumbersome and often impossible. This paper presents a comparison of a non-linear models based on Radial Basis Function Neural Network (RBFNN) and Hammerstein-Weiner Model (HW) and a conventional linear models, Auto regressive Integrated Moving Average (ARIMA) and Generalized Linear Regression (GLR) models for Kinta River, in Malaysia and Agra station of Yamuna river in India. Experimental data from the rivers were used in validating the models. Simulation results demonstrated that the nonlinear models (RBFNN and HW) averagely increased the performance accuracy of the linear models (ARIMA and GLR) by 20% and 15% at Kinta and Agra station of Yamuna River in the verification phase respectively. Considering the overall results the RBFNN model outperformed the other models with the average increase up to 21% in the verification phase. The model could serves as reliable and useful tool for forecasting the rivers.