Modelling of river flow using particle swarm optimized cascade-forward neural networks: A case study of kelantan river in malaysia

Abstract

Water resources management in Malaysia has become a crucial issue of concern due to its role in the economic and social development of the country. Kelantan river (Sungai Kelantan) basin is one of the essential catchments as it has a history of flood events. Numerous studies have been conducted in river basin modelling for the prediction of flow and mitigation of flooding events as well as water resource management. This paper presents river flow modelling based on meteorological and weather data in the Sungai Kelantan region using a cascade-forward neural network trained with particle swarm optimization algorithm (CFNNPSO). The result is compared with those trained with the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithm. The outcome of this study indicates that there is a strong correlation between river flow and some meteorological and weather variables (weighted rainfall, average evaporation and temperatures). The correlation scores (R) obtained between the target variable (river flow) and the predictor variables were 0.739, −0.544, and −0.662 for weighted rainfall, evaporation, and temperature, respectively. Additionally, the developed nonlinear multivariable regression model using CFNNPSO produced acceptable prediction accuracy during model testing with the regression coefficient (R2), root mean square error (RMSE), and mean of percentage error (MPE) of 0.88, 191.1 cms and 0.09%, respectively. The reliable result and predictive performance of the model is useful for decision makers during water resource planning and river management. The constructed modelling procedure can be adopted for future applications.