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Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia

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dc.citedby8
dc.contributor.authorAdli Zakaria M.N.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAbdul Malek M.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorHayet Khan M.M.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid58480232100en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57221404206en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid16304362800en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:18:25Z
dc.date.available2024-10-14T03:18:25Z
dc.date.issued2023
dc.description.abstractAccurate water level prediction for both lake and river is essential for flood warning and freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBoost were applied to develop water level forecasting models in Muda River, Malaysia. The models were developed using limited amount of daily water level and meteorological data from 2016 to 2018. Different input scenarios were tested to investigate the performance of the models. The results of the evaluation showed that the MLP model outperformed both the LSTM and the XGBoost models in predicting water levels, with an overall accuracy score of 0.871 compared to 0.865 for LSTM and 0.831 for XGBoost. No noticeable improvement has been achieved after incorporating meteorological data into the models. Even though the lowest reported performance was reported by the XGBoost, it is the faster of the three algorithms due to its advanced parallel processing capabilities and distributed computing architecture. In terms of different time horizons, the LSTM model was found to be more accurate than the MLP and XGBoost model when predicting 7 days ahead, demonstrating its superiority in capturing long-term dependencies. Therefore, it can be concluded that each ML model has its own merits and weaknesses, and the performance of different ML models differs on each case because these models depends largely on the quantity and quality of data available for the model training. � 2023 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe17689
dc.identifier.doi10.1016/j.heliyon.2023.e17689
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85164310557
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85164310557&doi=10.1016%2fj.heliyon.2023.e17689&partnerID=40&md5=d67d1dbe6bda2e19af3b5ac83837c937
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34204
dc.identifier.volume9
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleHeliyon
dc.subjectLSTM
dc.subjectMachine learning
dc.subjectMalaysia
dc.subjectMLP
dc.subjectMuda river
dc.subjectWater level
dc.subjectXGBoost
dc.titleExploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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