Publication:
Groundwater level forecasting with machine learning models: A review

Simple item page
dc.citedby19
dc.contributor.authorBoo K.B.W.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorOthman F.en_US
dc.contributor.authorKhan M.M.H.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid58742096300en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid36630785100en_US
dc.contributor.authorid16304362800en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2025-03-03T07:44:09Z
dc.date.available2025-03-03T07:44:09Z
dc.date.issued2024
dc.description.abstractGroundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo121249
dc.identifier.doi10.1016/j.watres.2024.121249
dc.identifier.scopus2-s2.0-85184785090
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85184785090&doi=10.1016%2fj.watres.2024.121249&partnerID=40&md5=8a440ed7daaf00792ea5e0ed3a32f9e3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36720
dc.identifier.volume252
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleWater Research
dc.subjectEnvironmental Monitoring
dc.subjectForecasting
dc.subjectGroundwater
dc.subjectMachine Learning
dc.subjectNeural Networks, Computer
dc.subjectground water
dc.subjectartificial neural network
dc.subjectenvironmental monitoring
dc.subjectforecasting
dc.subjectmachine learning
dc.subjectprocedures
dc.titleGroundwater level forecasting with machine learning models: A reviewen_US
dc.typeReviewen_US
dspace.entity.typePublication
Files
Collections
SCOPUS