Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition

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dc.authorid/0000-0003-1738-3565
dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.authoridElbeltagi, Ahmed/0000-0002-5506-9502
dc.contributor.authorLadouali, Sabrina
dc.contributor.authorKatipoglu, Okan Mert
dc.contributor.authorBahrami, Mehdi
dc.contributor.authorKartal, Veysi
dc.contributor.authorSakaa, Bachir
dc.contributor.authorElshaboury, Nehal
dc.contributor.authorKeblouti, Mehdi
dc.date.accessioned2024-12-24T19:27:02Z
dc.date.available2024-12-24T19:27:02Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractStudy region: Six regions in Algeria have been selected as follows: Ain Elhadjel, Msaad, Boussaada, Elkantara, M'sila and M'doukel. Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variational Mode Decomposition (VMD) technique and the Extreme Learning Machine (ELM) algorithm as a preprocessing technique for predicting future droughts. The first 6 and 12-month SPI values 1, 2, and 3-month lead time values were estimated with the ELM algorithm. After that, meteorological variables and Standard Precipitation Index (SPI) values, divided into subcomponents with VMD, are presented to the ELM model, and a drought forecasting model is developed. Model performances were evaluated according to various visual and statistical criteria. New hydrological insights for the region: Soft computing techniques have become the preferred method for producing predictions due to their ability to minimize development time, require minimal input, and offer a relatively less complex approach when compared to dynamic or physical models. As a result of the analysis, it has been determined that the highest prediction accuracies are generally obtained in VMD-ELM models and SPI predictions with a 1-month lead time. The study outputs give important ideas to mite donors regarding water resource planning and climate change adaptation strategies in the study area and can be applied to other arid and semi -arid environments.
dc.identifier.doi10.1016/j.ejrh.2024.101861
dc.identifier.issn2214-5818
dc.identifier.scopus2-s2.0-85195784246
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.ejrh.2024.101861
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6473
dc.identifier.volume54
dc.identifier.wosWOS:001255224600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Hydrology-Regional Studies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectStandard Precipitation Index (SPI)
dc.subjectDrought Forecasting
dc.subjectExtreme Machine Learning
dc.subjectData Decomposition
dc.subjectWater Resources Planning
dc.subjectClimate Change
dc.titleShort lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition
dc.typeArticle

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