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

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dc.citedby3
dc.contributor.authorLadouali S.en_US
dc.contributor.authorKatipo?lu O.M.en_US
dc.contributor.authorBahrami M.en_US
dc.contributor.authorKartal V.en_US
dc.contributor.authorSakaa B.en_US
dc.contributor.authorElshaboury N.en_US
dc.contributor.authorKeblouti M.en_US
dc.contributor.authorChaffai H.en_US
dc.contributor.authorAli S.en_US
dc.contributor.authorPande C.B.en_US
dc.contributor.authorElbeltagi A.en_US
dc.contributor.authorid59169490100en_US
dc.contributor.authorid57203751801en_US
dc.contributor.authorid57194685752en_US
dc.contributor.authorid57221197958en_US
dc.contributor.authorid55346347400en_US
dc.contributor.authorid57216611553en_US
dc.contributor.authorid55951389400en_US
dc.contributor.authorid55345356800en_US
dc.contributor.authorid57208073787en_US
dc.contributor.authorid57193547008en_US
dc.contributor.authorid57204724397en_US
dc.date.accessioned2025-03-03T07:42:34Z
dc.date.available2025-03-03T07:42:34Z
dc.date.issued2024
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. ? 2024 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo101861
dc.identifier.doi10.1016/j.ejrh.2024.101861
dc.identifier.scopus2-s2.0-85195784246
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85195784246&doi=10.1016%2fj.ejrh.2024.101861&partnerID=40&md5=ad87e9643b38a449f2cc8a8f7ec2e0df
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36468
dc.identifier.volume54
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleJournal of Hydrology: Regional Studies
dc.titleShort lead time standard precipitation index forecasting: Extreme learning machine and variational mode decompositionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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