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

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dc.citedby24
dc.contributor.authorNur Adli Zakaria M.en_US
dc.contributor.authorAbdul Malek M.en_US
dc.contributor.authorZolkepli M.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorid57222347567en_US
dc.contributor.authorid57221404206en_US
dc.contributor.authorid56429499300en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2023-05-29T09:06:36Z
dc.date.available2023-05-29T09:06:36Z
dc.date.issued2021
dc.descriptionDisasters; 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; Floodsen_US
dc.description.abstractA 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 AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2021.02.046
dc.identifier.epage4028
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85102410398
dc.identifier.spage4015
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85102410398&doi=10.1016%2fj.aej.2021.02.046&partnerID=40&md5=6e0f9f566b3e026d561511a4f18b3328
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26078
dc.identifier.volume60
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.titleApplication of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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