Data-driven drought prediction by means of machine learning techniques and ıncreasing accuracy with wavelet transform

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dc.authorid0000-0001-7547-847X
dc.authorid0000-0002-9954-9794
dc.authorid0000-0002-5593-5538
dc.authorid0000-0003-2906-0771
dc.contributor.authorTuğrul, Türker
dc.contributor.authorSelek, Bülent
dc.contributor.authorHınıs, Mehmet Ali
dc.contributor.authorSelek, Zeliha
dc.contributor.authorOruç, Sertaç
dc.date.accessioned2025-09-19T10:59:59Z
dc.date.available2025-09-19T10:59:59Z
dc.date.issued2025
dc.departmentMühendislik Fakültesi
dc.description.abstractDrought is not only a problem that challenges scientists but also one of the most difficult natural disasters to combat for local governments and decision-makers. Like many parts of the world suffering from drought, the western Mediterranean region of Turkey is also affected by drought. In this study, innovative drought prediction models were created with different machine learning algorithms and deep learning methods to create a model that will help decision-makers regarding drought. 4 different monthly lagged model structures were established using SPI12 values calculated with precipitation data between 1967 and 2020 for the Acipayam, Bodrum and Fethiye regions located in the west of Turkey. While providing data, attention was paid to the distance between stations and data continuity. The models were analyzed with Long-Short Term Memory (LSTM), Artificial Neural Network (ANN) and Random Forest (RF) algorithms. In addition, Discrete Wavelet Transform (WT) was used to obtain better model results. The hyper-parameters of these algorithms were determined by taking into account the most commonly used parameters in the literature. The analysis results were evaluated by correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe Efficiency (NSE), and Combined Accuracy (CA). As a result of the comparison of these methods, the best results were obtained in the M01 model of LSTM, LSTM-WM01, for Acipayam after WT (r: 0.9910, NSE: 0.9733, RMSE: 0.1637, and CA: 0.1030). While the best prediction for Bodrum was obtained in LSTM-WM02 after WT (r:0.9657, NSE:0.9325, RMSE:0.3101, and CA: 0.1929), for Fethiye it was obtained in LSTM-WM02 having performance metrics r:0.9539, NSE:0.8973, RMSE:0.3689, and CA: 0.2359. It is expected that the results obtained with this study will help researchers and decision-making authorities on drought.
dc.identifier.doi10.1007/s00024-025-03800-4
dc.identifier.issn00334553
dc.identifier.scopus2-s2.0-105014758160
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s00024-025-03800-4
dc.identifier.urihttps://hdl.handle.net/20.500.12451/14502
dc.identifier.wosWOS:001560293200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorHınıs, Mehmet Ali
dc.institutionauthorid0000-0002-1870-3535
dc.language.isoen
dc.publisherBirkhauser Verlag Basel
dc.relation.ispartofPure and Applied Geophysics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectANN
dc.subjectDrought
dc.subjectLSTM
dc.subjectRF
dc.subjectSoft Computing
dc.subjectSPI
dc.titleData-driven drought prediction by means of machine learning techniques and ıncreasing accuracy with wavelet transform
dc.typeArticle

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