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Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion

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dc.citedby8
dc.contributor.authorAhmed Osman A.I.en_US
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorWee Boo K.B.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57221644207en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid58939630300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2025-03-03T07:43:14Z
dc.date.available2025-03-03T07:43:14Z
dc.date.issued2024
dc.description.abstractDue to the impact of climate change, the groundwater level (GWL) has been declining recently in Malaysia, which is essential to protect the groundwater aquifer against depletion. Therefore, the current study aimed to propose an accurate GWL prediction model using advanced machine learning (ML) algorithms in five populated towns, namely Jenderam, Bangi, Beranang, Kajang, and Paya Indah Wetland which are in Selangor, Malaysia. The models developed, used 11 months of previously recorded daily meteorological data of rainfall, temperature, evaporation, and GWL data from one selected well for each town, to predict 1-day, 3-day, and 5-day horizons GWL. For all five locations, four ML algorithms have been trained, tested, and then evaluated: long short-term memory (LSTM), extreme gradient boost (XGBoost), Artificial Neural Network (ANN), and Support Vector Regression (SVR). Further, the best model among the four proposed models is used to predict daily GWL from January 2030 to December 2039 using projected rainfall and temperature data extracted from the Intercomparison Project Phase 5 (CMIP5) climate model. Applying the same 3 different input combinations for the models, the results showed that all the locations, including the GWL time-series data, improved the prediction accuracy significantly in all four models. Using testing data for 1-day ahead GWL prediction at Paya Indah Wetland as the best-performing location, XGBoost achieved the highest prediction performance with root mean squared error (RMSE) of 0.026 followed by LSTM, ANN, and SVR with RMSE of 0.027, 0.050, and 0.085 respectively. Ultimately, the results obtained in this study serve as a great benchmark for future GWL prediction using LSTM and XGBoost algorithm and give an insight into the influence of climate change on future GWL. Further, the findings can help local water resource managers draft resealable accuracy water resource plans in the state of Selangor for the next decade. ? 2024 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo101152
dc.identifier.doi10.1016/j.gsd.2024.101152
dc.identifier.scopus2-s2.0-85187784558
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85187784558&doi=10.1016%2fj.gsd.2024.101152&partnerID=40&md5=6c1d0a9affa61e24479852e4319719b6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36586
dc.identifier.volume25
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleGroundwater for Sustainable Development
dc.subjectMalaysia
dc.subjectAquifers
dc.subjectClimate change
dc.subjectClimate models
dc.subjectForecasting
dc.subjectGroundwater resources
dc.subjectHydrogeology
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectLocation
dc.subjectLong short-term memory
dc.subjectMean square error
dc.subjectRain
dc.subjectSupport vector machines
dc.subject'current
dc.subjectGround water level
dc.subjectGroundwater aquifer
dc.subjectMachine learning algorithms
dc.subjectMachine-learning
dc.subjectMalaysia
dc.subjectPrediction modelling
dc.subjectRoot mean squared errors
dc.subjectSupport vector regressions
dc.subjectWaters resources
dc.subjectaquifer
dc.subjectclimate change
dc.subjectgroundwater resource
dc.subjectmachine learning
dc.subjectprediction
dc.subjectregression analysis
dc.subjectWetlands
dc.titleAdvanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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