Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

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dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.authoridKARAKOYUN, ERKAN/0000-0003-2821-9103
dc.authoridAKINER, MUHAMMED ERNUR/0000-0002-5192-2473
dc.contributor.authorAkiner, Muhammed Ernur
dc.contributor.authorKartal, Veysi
dc.contributor.authorGuzeler, Anil Can
dc.contributor.authorKarakoyun, Erkan
dc.date.accessioned2024-12-24T19:24:56Z
dc.date.available2024-12-24T19:24:56Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractThe Ye & scedil;il & imath;rmak River Basin in northern T & uuml;rkiye is crucial for the region's water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Ye & scedil;il & imath;rmak River basin (R2 = 0.98, RMSE = 0.18). The study's findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.
dc.description.sponsorshipSiirt University; State Water Works
dc.description.sponsorshipSpecial thanks to the General Directorate of Meteorology (MGM) and State Water Works (DSI) for providing the database used in this study.
dc.identifier.doi10.1007/s12145-024-01332-4
dc.identifier.endpage3135
dc.identifier.issn1865-0473
dc.identifier.issn1865-0481
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85193781732
dc.identifier.scopusqualityQ2
dc.identifier.startpage3111
dc.identifier.urihttps://doi.org/10.1007/s12145-024-01332-4
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6202
dc.identifier.volume17
dc.identifier.wosWOS:001230362200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofEarth Science Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectArtificial neural network
dc.subjectDesign of experiments
dc.subjectLong short-term memory
dc.subjectRiver flow rate
dc.subjectSliding window methodology
dc.titleExploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation
dc.typeArticle

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