###### 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 (\(R^{2}\)), 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. **Keywords:** river flow modelling; cascade-forward neural networks; particle swarm optimization; multivariable regression; Malaysia river +
Footnote †: journal: Applied  
the quality of river water is threatened by various factors [3]. Physicochemical and biological indicators have been used to assess and estimate the quality of river water [4]. Another important aspect of hydrology in Malaysia is water resource management. Water resource management can be defined as a procedure for evaluating the scope, source, quality, and amount of water resources for adequate water resource management and utilization. In the aspect of quantity, artificial neural networks (ANNs) have been employed in previous studies for the prediction of river flow modelling in Malaysia and other countries [5]. For instance, in the case of Malaysian rivers, [PERSON] et al. [6] applied a radial basis function (RBF) neural network in forecasting the suspended sediment (SS) discharge of the Pari River. The outcome of the study showed that the RBF neural network models are adequate and they can forecast the nonlinear activity of the suspended solid discharge. [PERSON] and [PERSON] [7] applied cascade-forward backpropagation neural networks to predict rain rate, radar reflectivity and water content with raindrop size distribution. The research was conducted in five localities of African countries; Cote d'Ivoire, Cameroon, Senegal, Congo-Brazaville and Niger. Furthermore, the performance of two ANNs such as RBF and feed-forward neural networks (FFNN) has been compared in the study of Rantau Panjang streamflow station, Sungai Johor [8]. The result indicated that the FFNN model gave a better performance in estimating the sediment load compared to the RBF model. [PERSON] and [PERSON] [9] compared two other ANNs, namely, Multi-Layer Perceptron (MLP) and RBF for predicting sediment load at Langat River. However, MLP showed better performance, although both ANNs models have demonstrated limited effectiveness in estimating large sediment loads. Similarly, [PERSON] et al. [10] compared the performance of Multiple Linear Regression (MLRg) and ANN in the prediction of SS discharge of the Jendearam catchment area.