Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Basit Öğe Kaydı
dc.authoridYurtsever, Adem/0000-0001-6512-5232
dc.authoridKilinc, Huseyin Cagan/0000-0003-1848-2856
dc.contributor.authorKılınç, Hüseyin Çağan
dc.contributor.authorYurtsever, Adem
dc.date.accessioned2025-03-26T17:34:45Z
dc.date.available2025-03-26T17:34:45Z
dc.date.issued2022
dc.departmentİstanbul Esenyurt Üniversitesi, Fakülteler, Mühendislik ve Mimarlık Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractThe effects of developing technology and rapid population growth on the environment have been expanding gradually. Particularly, the growth in water consumption has revealed the necessity of water management. In this sense, accurate flow estimation is important to water management. Therefore, in this study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model is proposed for streamflow forecasting. In the study, daily flow data of uctepe and Tuzla flow observation stations located in various water collection areas of the Seyhan basin were utilized. In the test and training analysis of the models, the first 75% of the data were used for training, and the remaining 25% for testing. The accuracy and success of the hybrid model were compared via the comparison model and linear regression, one of the most basic models of artificial neural networks. The estimation results of the models were analyzed using different statistical indexes. Better results were obtained for the GWO-GRU hybrid model compared to the benchmark models in all statistical metrics except SD at the uctepe station and the whole Tuzla station. At uctepe, the FMS, despite the RMSE and MAE of the hybrid model being 82.93 and 85.93 m(3)/s, was 124.57 m(3)/s, and it was 184.06 m(3)/s in the single GRU model. We achieved around 34% and 53% improvements, respectively. Additionally, the R-2 values for Tuzla FMS were 0.9827 and 0.9558 from GWO-GRU and linear regression, respectively. It was observed that the hybrid GWO-GRU model could be used successfully in forecasting studies.
dc.identifier.doi10.3390/su14063352
dc.identifier.issn2071-1050
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85126927226
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su14063352
dc.identifier.urihttps://hdl.handle.net/20.500.14704/880
dc.identifier.volume14
dc.identifier.wosWOS:000774585300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250326
dc.subjecttime series; streamflow; grey wolf optimization; gated recurrent unit; forecasting
dc.titleShort-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Mühendislik ve Mimarlık Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu