Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers

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dc.contributor.authorAchite, Mohammed
dc.contributor.authorKatipoglu, Okan Mert
dc.contributor.authorKartal, Veysi
dc.contributor.authorSarigol, Metin
dc.contributor.authorJehanzaib, Muhammad
dc.contributor.authorGul, Enes
dc.date.accessioned2026-04-04T13:31:12Z
dc.date.available2026-04-04T13:31:12Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractThe rising incidence of droughts in specific global regions in recent years, primarily attributed to global warming, has markedly increased the demand for reliable and accurate streamflow estimation. Streamflow estimation is essential for the effective management and utilization of water resources, as well as for the design of hydraulic infrastructure. Furthermore, research on streamflow estimation has gained heightened importance because water is essential not only for the survival of all living organisms but also for determining the quality of life on Earth. In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network-recurrent neural network (CNN-RNN), and group method of data handling (GMDH) algorithms, were employed to forecast monthly streamflow time series at two different stations in the Wadi Mina basin. The performance of each technique was evaluated using statistical criteria such as mean square error (MSE), mean bias error (MBE), mean absolute error (MAE), and the correlation coefficient (R). The results of this study demonstrated that the GMDH algorithm produced the most accurate forecasts at the Sidi AEK Djillali station, with metrics of MSE: 0.132, MAE: 0.185, MBE: -0.008, and R: 0.636. Similarly, the CNN-RNN algorithm achieved the best performance at the Kef Mehboula station, with metrics of MSE: 0.298, MAE: 0.335, MBE: -0.018, and R: 0.597.
dc.identifier.doi10.3390/atmos16010106
dc.identifier.issn2073-4433
dc.identifier.issue1
dc.identifier.orcid0000-0003-4671-1281
dc.identifier.orcid0000-0001-9364-9738
dc.identifier.orcid0000-0001-6421-6087
dc.identifier.orcid0000-0001-6084-5759
dc.identifier.orcid0000-0002-5556-3738
dc.identifier.scopus2-s2.0-85216028064
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/atmos16010106
dc.identifier.urihttps://hdl.handle.net/11616/108654
dc.identifier.volume16
dc.identifier.wosWOS:001404024000001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofAtmosphere
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectdeep learning
dc.subjectdrought
dc.subjectsoft computing
dc.subjectGMDH
dc.subjectstreamflow
dc.subjectprediction
dc.titleAdvanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers
dc.typeArticle

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