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Groundwater level as an input to monthly predicting of water level using various machine learning algorithms

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dc.citedby6
dc.contributor.authorSapitang M.en_US
dc.contributor.authorRidwan W.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57215211508en_US
dc.contributor.authorid57218502036en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:06:11Z
dc.date.available2023-05-29T09:06:11Z
dc.date.issued2021
dc.descriptionalgorithm; ground penetrating radar; groundwater; machine learning; resource management; water level; water resourceen_US
dc.description.abstractAccurate prediction of the water level will help prevent overexploiting groundwater and help control water resources. On the other hand, water level predicting is a highly dynamic and non-linear process dependent on complex factors. Therefore, developing models to predict water levels to optimize water resources management in the reservoir is essential. Thus, this work recommends various supervised machine learning algorithms for predicting water levels with groundwater level correlation. The predicting models have Linear Regression (LR), Support Vector Machines (SVM), Gaussian Processes Regression (GPR), and Neural Network (NN). This study includes four scenarios; The first scenario (SC1) uses lag 1; second scenario (SC2) uses lag 1 and lag 2; third scenario (SC3) uses lag 1, lag 2, and lag 11 and the fourth scenario (SC4) uses lag 1, lag 2, lag 11 and lag 12. These scenarios have been determined using the autocorrelation function (ACF), and these lags represent the month. The results showed that for SC1, SC2, and SC4, all model performance in GPR gave good results where the highest R equal to 0.71 in SC1, 0.78 in SC2, and 0.73 in SC4 using the Matern 5/2 GPR model. For SC3, the Stepwise LR model gave a better result with an R of 0.79. It can be concluded that Matern 5/2 of Gaussian Processes Regression Models is a�reliable model to predict water level as the method gave a high performance in each scenario (except SC3) with a relatively fastest training time. The NN model had the worst performance to the other three models since it has the highest MAE values, RMSE, and lowest value of R in almost all four scenarios of input combinations. These results obtained in this study serves as an excellent benchmark for future water level prediction using the GPR and LR with four scenarios created. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s12145-021-00654-x
dc.identifier.epage1283
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85109300934
dc.identifier.spage1269
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85109300934&doi=10.1007%2fs12145-021-00654-x&partnerID=40&md5=1279c0d304365487e982b3d1553a438f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26027
dc.identifier.volume14
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEarth Science Informatics
dc.titleGroundwater level as an input to monthly predicting of water level using various machine learning algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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