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Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia

Disasters; Flood control; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Membership functions; Network layers; Rivers; Sensitivity analysis; Uncertainty analysis; Water levels; Weather forecasting; Adaptive neuro-fuzzy inference; Adaptive neuro-fuzzy inference system, multi-layer perceptron...

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Main Authors:	Nur Adli Zakaria M., Abdul Malek M., Zolkepli M., Najah Ahmed A.
Other Authors:	57222347567
Format:	Article
Published:	Elsevier B.V. 2023
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id	my.uniten.dspace-26078
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spelling	my.uniten.dspace-260782023-05-29T17:06:37Z Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia Nur Adli Zakaria M. Abdul Malek M. Zolkepli M. Najah Ahmed A. 57222347567 57221404206 56429499300 57214837520 Disasters; Flood control; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Membership functions; Network layers; Rivers; Sensitivity analysis; Uncertainty analysis; Water levels; Weather forecasting; Adaptive neuro-fuzzy inference; Adaptive neuro-fuzzy inference system, multi-layer perceptron neural network; Flood forecasting; Leadtime; Malaysia; Multilayer perceptrons neural networks (MLPs); Neuro-fuzzy inference systems; River levels; River water; Short-term forecasting; Floods A reliable river water level model to forecast the changes in different lead times is vital for flood warning systems, especially in countries like Malaysia, where flood is considered the most devastating natural disaster. In the current study, the ability of two artificial intelligence (AI) based data-driven approaches: Multi-layer Perceptron Neural Networks (MLP-NN) and An Adaptive Neuro-Fuzzy Inference System (ANFIS), as reliable models in forecasting the river level based on an hourly basis are investigated. 10-year of hourly measured data of the Muda river's water level in the northern part of Malaysia is used for training and testing the proposed models. Different statistical indices are introduced to validate the reliability of the models. Optimizing the hyper-parameters for both models is explored. Then, sensitivity analysis and uncertainty analysis are carried out. Finally, the capability of the models to forecast the river level for different lead times (1, 3, 6, 9, 12, and 24-hours ahead) is investigated. The results reveal that a high accuracy was achieved for the MLP-NN model with 4 hidden neurons with RMSE (0.01740), while for ANFIS, a model with three G-bell shaped membership functions outperformed other ANFIS models with RMSE (0.0174). MLP-NN and ANFIS achieved a high level of performance when two input combinations were used with RMSE equal to 0.01299 and 0.0130, respectively. However, MLP outperformed ANFIS in terms of running time and the uncertainty analysis test, in which the d-factor is found to be 0.000357. � 2021 THE AUTHORS Final 2023-05-29T09:06:36Z 2023-05-29T09:06:36Z 2021 Article 10.1016/j.aej.2021.02.046 2-s2.0-85102410398 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102410398&doi=10.1016%2fj.aej.2021.02.046&partnerID=40&md5=6e0f9f566b3e026d561511a4f18b3328 https://irepository.uniten.edu.my/handle/123456789/26078 60 4 4015 4028 All Open Access, Gold Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Disasters; Flood control; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Membership functions; Network layers; Rivers; Sensitivity analysis; Uncertainty analysis; Water levels; Weather forecasting; Adaptive neuro-fuzzy inference; Adaptive neuro-fuzzy inference system, multi-layer perceptron neural network; Flood forecasting; Leadtime; Malaysia; Multilayer perceptrons neural networks (MLPs); Neuro-fuzzy inference systems; River levels; River water; Short-term forecasting; Floods
author2	57222347567
author_facet	57222347567 Nur Adli Zakaria M. Abdul Malek M. Zolkepli M. Najah Ahmed A.
format	Article
author	Nur Adli Zakaria M. Abdul Malek M. Zolkepli M. Najah Ahmed A.
spellingShingle	Nur Adli Zakaria M. Abdul Malek M. Zolkepli M. Najah Ahmed A. Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia
author_sort	Nur Adli Zakaria M.
title	Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia
title_short	Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia
title_full	Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia
title_fullStr	Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia
title_full_unstemmed	Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia
title_sort	application of artificial intelligence algorithms for hourly river level forecast: a case study of muda river, malaysia
publisher	Elsevier B.V.
publishDate	2023
_version_	1806426140388098048
score	13.211869

Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia

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