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.

Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM

“…The third approach is promising for few-step-ahead prediction, but it needs larger data to build the model that is able to perform longer-step-ahead forecasting. Future work will involve collecting more data and also investigating optimization of hyperparameters of the machine learning/deep learning-based model such as using grid search or meta-heuristic optimization, which has been done earlier in another study (Hayder et al 2020). Applications of ensemble and boosting machine learning such as random forest and gradient boosting algorithms can be explored as well.…”

“…The stage of preliminary data analysis and pre-processing is crucial in the initial stage of the machine learning model building. This process can significantly affect the prediction accuracy in any type of data (Hayder et al 2020). The purpose of this study is to build a multi-step-ahead predictive model for RF using a machine learning/deep learning-based approach.…”

On the Determination of Groundwater Level using Temporal and Spatial Parameters: Advanced Machine Learning Methods

“…There is a direct relationship between input and output in the perceptron connection, whereas in the feedforward neural network connection, there is an indirect relationship between input and output, which is a hidden layer through a nonlinear activation function (Fahlman and Lebiere 1989). If the connection form is combined in a multilayer network and perceptron, the network can be formed with a direct connection and the indirect connection between the input layer and the output layer (Hayder et al 2020). The network formed of this connection pattern is called cascade forward neural network (CF), and the equations of this model can be written as follows:…”

“…Kouziokas et al (2018) used MLP to predict groundwater levels in Montgomery, Pennsylvania. Hayder et al (2020) modeled river flow using optimized CF and MLP in the Kelantan River in Malaysia. Hong et al (2020) predicted trihalomethanes levels in tap water using RBF and gray relational analysis (GRA).…”

A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems

“…The objective of this paper was to develop sediment load estimation from available input predictor variables using various machine learning (ML) algorithms as a powerful AI approach. The ML algorithms have been applied in numerous hydrology studies such as in (Hayder et al, 2020) where ANN is used. Some studies also used ANN particularly for sediment prediction, such as (Olyaie et al, 2015) (Afan et al, 2014) (Nivesh & Kumar, 2017).…”