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Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques

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dc.citedby3
dc.contributor.authorPathan A.I.en_US
dc.contributor.authorSidek L.B.M.en_US
dc.contributor.authorBasri H.B.en_US
dc.contributor.authorHassan M.Y.en_US
dc.contributor.authorKhebir M.I.A.B.en_US
dc.contributor.authorOmar S.M.B.A.en_US
dc.contributor.authorKhambali M.H.B.M.en_US
dc.contributor.authorTorres A.M.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorid57209510674en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid57065823300en_US
dc.contributor.authorid57932252900en_US
dc.contributor.authorid57485745900en_US
dc.contributor.authorid57964112400en_US
dc.contributor.authorid58934910500en_US
dc.contributor.authorid54963844600en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2025-03-03T07:42:41Z
dc.date.available2025-03-03T07:42:41Z
dc.date.issued2024
dc.description.abstractMachine learning (ML) techniques are rapidly emerging as effective tools in predicting complex hydrological processes. The present study aims to comparatively assess the efficacy of four machine learning algorithms ? Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF) ? in predicting water levels using rainfall data at the Batu Dam, Malaysia. Situated about 16 km from Kuala Lumpur city center, the Batu Dam plays a crucial role in flood mitigation and water supply. Utilizing a statistical approach, the models were evaluated based on key performance metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). Preliminary results accentuated the superior predictive prowess of the MLP model, especially for challenging forecasting scenarios with longer lag intervals. This investigation not only accentuates the potential of data-driven methodologies in hydrology but also offers valuable insights for water resource management in the region. When all scenarios for the MLP model are considered, it is observed that the 3-day scenario performed the best within MLP, with the lowest RMSE (at 0.0072) and MAE (at 0.005), and the highest R2 score (at 0.9972). Furthermore, within the MLP model. Due to its exceptionally high performance, the MLP-3 model proved to be an excellent choice for our modeling purposes. Furthermore, it was observed that MLP-3 yields a high R2 score of 0.994, and its predictions aligned closely with the actual water level values. This indicates that the model fits very well to the modeling problem. On the other hand, the SVR-30 model had an R2 score of 0.83, and its predictions were quite scattered with respect to the actual water levels. Four different input scenarios were investigated, considering correlation analysis. Generally, the comparison of four ML model indicated that the MLP model offered better accuracy in predicting daily water levels with respect to different assessment criteria. The findings of this study depicted the accomplishment of MLP model in capturing the changes in the water level of a dam thus paving the way for which the model can be used in works to mitigate potential risk that may occur in the future from natural events. ? 2024 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102854
dc.identifier.doi10.1016/j.asej.2024.102854
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85193432621
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85193432621&doi=10.1016%2fj.asej.2024.102854&partnerID=40&md5=05bbafe1df910a950dbcefa25735bf26
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36489
dc.identifier.volume15
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.subjectAdaptive boosting
dc.subjectDams
dc.subjectForecasting
dc.subjectForestry
dc.subjectHydrology
dc.subjectInformation management
dc.subjectMean square error
dc.subjectRain
dc.subjectSupport vector machines
dc.subjectWater management
dc.subjectWater supply
dc.subjectComparative assessment
dc.subjectEffective tool
dc.subjectHydrological process
dc.subjectMachine learning techniques
dc.subjectMachine-learning
dc.subjectMean absolute error
dc.subjectMultilayers perceptrons
dc.subjectRoot mean squared errors
dc.subjectSupport vector regressions
dc.subjectWater level prediction
dc.subjectWater levels
dc.titleComparative assessment of rainfall-based water level prediction using machine learning (ML) techniquesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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