Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin

Nurul Najihah Zulkiflee - Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
Noor Zuraidin Mohd Safar - Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
Hazalila Kamaludin - Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
Muhamad Hanif Jofri - Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
Noraziahtulhidayu Kamarudin - Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
Rayidah Rasyidah - Politeknik Negeri Padang, Padang, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.2.2704

Abstract


This research delves into the effectiveness of Artificial Neural Networks with Multilayer Perceptron (ANN-MLP) and Nonlinear AutoRegressive with eXogenous inputs (NARX) models in predicting short-term rainfall-runoff patterns in the Batu Pahat River Basin. This study aims to predict river water levels using historical rainfall and river level data for future intervals of 1, 3, and 6 hours. Data preprocessing techniques, including the management of missing values, identification of outliers, and reduction of noise, were applied to enhance the accuracy and dependability of the models. This study assessed the performance of the models for ANN-MLP and NARX by comparing their effectiveness across various forecast timeframes and evaluating their performance in different scenarios. The findings of the study revealed that the ANN-MLP model showed robust performance in short-term prediction. On the contrary, the NARX model exhibited higher accuracy, particularly in capturing intricate temporal relationships and external impacts on river behavior. The ANN-MLP produces 99% accuracy for 1-hour prediction, and NARX yields 98% accuracy with 0.3245 Root Mean Squared Error and 0.1967 Mean Absolute Error. This study makes a valuable contribution to hydrological forecasting by presenting a rigorous and precise modeling methodology.

Keywords


rainfall-runoff simulation; Artificial neural network; Hydrological model; ANN; NARX

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