Comparison of LSTM and SVM methods through wavelet decomposition in drought forecasting

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dc.authorid0000-0001-7547-847X
dc.authorid0000-0002-1870-3535
dc.authoridh0000-0003-2906-0771
dc.contributor.authorTuğrul, Türker
dc.contributor.authorHınıs, Mehmet Ali
dc.contributor.authorOruç, Sertaç
dc.date.accessioned2025-07-16T12:56:48Z
dc.date.available2025-07-16T12:56:48Z
dc.date.issued2025
dc.departmentMühendislik Fakültesi
dc.description.abstractMany researchers are working to prevent, monitor and identify drought, which is one of the most insidious and dangerous natural disasters that negatively affects life. For this purpose, various drought indices are developed and new methods are proposed. One of the most widely used of these indexes is the Standard Precipitation Index (SPI). Since it is not known when the drought will begin, taking preventive measures is a difficult and challenging task. In the last decade, machine learning techniques have been preferred to increase success in predicting droughts. In this study, SPI was used as the drought index and Support Vector Machine (SVM) and Long-Short Term Memory Network (LSTM) methods, which are increasingly reliable among the most preferred machine learning and deep learning methods in drought predictions, were used as the prediction method, and furthermore, to increase the prediction power of these methods, new powerful models have been proposed using Wavelet transform and Variational mode transform. Support Vector Machines with Wavelet decomposition (SVM-W), Long-Short Term Memory Networks with Wavelet decomposition (LSTM-W), and Long Short Term Memory Networks with Variational Mode Decomposition (LSTM-VMD) were used as prediction models for drought analysis and the performances of these models were compared.
dc.identifier.doi10.1007/s12145-024-01541-x
dc.identifier.issn18650473
dc.identifier.issue1
dc.identifier.scopus85214114154
dc.identifier.urihttps://dx.doi.org/10.1007/s12145-024-01541-x
dc.identifier.urihttps://hdl.handle.net/20.500.12451/13333
dc.identifier.volume18
dc.identifier.wosWOS:001389252000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorHınıs, Mehmet Ali
dc.institutionauthorid0000-0002-1870-3535
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofEarth Science Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDam Management
dc.subjectDrought Modeling
dc.subjectLSTM
dc.subjectRisk Assessment
dc.subjectSoft Computing
dc.subjectSVM
dc.titleComparison of LSTM and SVM methods through wavelet decomposition in drought forecasting
dc.typeArticle

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