Evaluating Performances of LSTM, SVM, GPR, and RF for Drought Prediction in Norway: A Wavelet Decomposition Approach on Regional Forecasting

dc.authorid0000-0003-2906-0771
dc.authorid0000-0002-1870-3535
dc.authorid0000-0001-7547-847X
dc.contributor.authorOruç, Sertaç
dc.contributor.authorHınıs, Mehmet Ali
dc.contributor.authorTuğrul, Türker
dc.date.accessioned2025-02-24T06:53:22Z
dc.date.available2025-02-24T06:53:22Z
dc.date.issued2024
dc.departmentMühendislik Fakültesi
dc.description.abstractA serious natural disaster that poses a threat to people and their living spaces is drought, which is difficult to notice at first and can quickly spread to wide areas through subtle progression. Numerous methods are being explored to identify, prevent, and mitigate drought, and distinct metrics have been developed. In order to contribute to the research on measures to be taken against drought, the Standard Precipitation Evaporation Index (SPEI), one of the drought indices that has been developed and accepted in recent years and includes a more comprehensive drought definition, was chosen in this study. Machine learning and deep learning algorithms, including support vector machine (SVM), random forest (RF), long short-term memory (LSTM), and Gaussian process regression (GPR), were used to model the droughts in six regions of Norway: Bodø, Karasjok, Oslo, Tromsø, Trondheim, and Vadsø. Four distinct model architectures were employed for this goal, and as a novel approach, the models’ output was enhanced by using discrete wavelet decomposition/transformation (WT). The model outputs were evaluated using the correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) as performance evaluation criteria. When the findings were analyzed, the GPR model (W-GPR), which was acquired after WT, typically produced the best results. Furthermore, it was discovered that, out of all the recognized models, M04 had the most effective model structure.
dc.identifier.doi10.3390/w16233465
dc.identifier.issn2073-4441
dc.identifier.issue23en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://dx.doi.org/10.3390/w16233465
dc.identifier.urihttps://hdl.handle.net/20.500.12451/12941
dc.identifier.volume16en_US
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofWater (Switzerland)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectGlobal Change
dc.subjectExtreme Weather
dc.subjectMachine Learning
dc.subjectDeep Learning
dc.subjectRisk Assessment
dc.subjectSPEI
dc.titleEvaluating Performances of LSTM, SVM, GPR, and RF for Drought Prediction in Norway: A Wavelet Decomposition Approach on Regional Forecasting
dc.typeArticle

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