Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks

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dc.contributor.authorGharehbaghi, Amin
dc.contributor.authorGhasemlounia, Redvan
dc.contributor.authorAhmadi, Farshad
dc.contributor.authorAlbaji, Mohammad
dc.date.accessioned2024-06-13T20:18:02Z
dc.date.available2024-06-13T20:18:02Z
dc.date.issued2022
dc.departmentFakülteler, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractPrecise estimation of groundwater level (GWL) fluctuations has a substantial effect on water resources management. In the present study, to forecast the regional mean monthly time series groundwater level (GWL) with a range of 4.82 (m) in Urmia plain, three different layer structures of Gated Recurrent Unit (GRU) deep learningbased neural network models via the module of sequence-to-sequence regression are designed. In this sense, 180time series datasets of regional mean monthly meteorological, hydrological, and observed water table depths of 42 different monitoring piezometers during the period of Oct 2002-Sep 2017 are employed as the input variables. By using Shannon entropy method, the most influential parameters on GWL are determined as regional mean monthly air temperature (Tam), precipitation (Pm), total (sum) water diversion discharge (Wdm) of four main rivers. Nevertheless, Cosine amplitude sensitivity analysis confirmed Tam as a dominant factor. For preventing overfitting problem, an algorithm tuning technique via different kinds of hyperparameters is operated. In this respect, several scenarios are implemented and the optimal hyperparameters are accomplished via the trialand-error process. As stated by the performance evaluation metrics, Model Grading process, and Total Learnable Parameters (TLP) value, the innovative and unique suggested model (3), entitled GRU2+, (Double-GRU model coupled with Addition layer (+)) with seven layers is carefully chosen as the best model. The unique suggested model (3) in the optimal hyperparameters, resulted in an R2 of 0.91, a total grade (TG) of 7.76, an RMSE of 0.094 (m), and a running time of 47 (s). Thus, the model (3) can be certainly employed as an effective model to forecast GWL in different agricultural areas.
dc.description.sponsorshipHasan Kalyoncu University ; Shahid Chamran University of Ahvaz
dc.identifier.doi10.1016/j.jhydrol.2022.128262
dc.identifier.issn0022-1694
dc.identifier.issn1879-2707
dc.identifier.scopus2-s2.0-85135395179
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jhydrol.2022.128262
dc.identifier.urihttps://hdl.handle.net/11501/1178
dc.identifier.volume612
dc.identifier.wosWOS:000861036700006
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGhasemlounia, Redvan
dc.institutionauthorid0000-0003-1796-4562
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Hydrology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectRegional Mean Monthly Groundwater Level
dc.subjectGru Neural Network
dc.subjectShannon Entropy Method
dc.subjectCosine Amplitude Sensitivity Analysis
dc.subjectWater-Table Depth
dc.subjectModel
dc.titleGroundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks
dc.typeArticle

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