Non-Linear Output Structure Learning: A Novel Multi-Target Technique for Multi-Station and Multi-Index Drought Modelling

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dc.authorid0000-0002-4767-6660
dc.authorid0000-0003-0559-5261
dc.authorid0000-0003-0189-4774
dc.contributor.authorSafari, Mir Jafar Sadegh
dc.contributor.authorArashloo, Shervin Rahimzadeh
dc.contributor.authorVaheddoost, Babak
dc.date.accessioned2026-02-08T15:14:46Z
dc.date.available2026-02-08T15:14:46Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractExiting artificial intelligence-based drought models estimate a single drought index in a single station. This study advances drought modelling by proposing Non-linear Output Structure Learning (NOSL) for simultaneously estimating two drought indices at eight stations. A multi-target drought model provides insights for a better understanding of the meteorological and hydrological impacts of drought. Hydro-meteorological data, including precipitation, evaporation, and streamflow, are used for a joint estimation of Streamflow Drought Index (SDI) and Standardized Precipitation Evapotranspiration Index (SPEI). The efficacy of the NOSL algorithm is examined against single-target Kernel Ridge Regression (KRR) and Fast Multi-output Relevance Vector Regression (FMRVR) models. The data during October 1981 to September 2015 at a monthly scale (408 Months) from eight different stations in Buyuk Menderes Basin (BMB) located (BMB) in Western T & uuml;rkiye are used in this study. The effects of 1-, 3-, and 6-month Moving Average (MA) are also considered for drought estimation. Results show that NOSL can effectively estimate the SPEI and SDI indices and outperforms KRR and FMRVR benchmarks. The effectiveness of the NOSL technique can be linked to a structural modelling mechanism based on vector-valued functions, where the dependencies among output variables are captured utilising a non-linear function for enhanced performance. The developed multi-target drought model based on the NOSL technique not only helps in incorporating multiple variables in the model for a better estimation, but it enhances our understanding of various aspects of droughts and building adaptive strategies and resilience map counter to drought hazard.
dc.description.sponsorshipYasar University Project Evaluation Commission (PEC) [BAP 133]
dc.description.sponsorshipThis publication is supported as part of Project No. BAP 133 entitled Future of Hydro-meteorological Droughts in the Aegean Region with Respect to the Climate Change Scenarios has been approved by the Yasar University Project Evaluation Commission (PEC) under the coordination of the first author (M.J.S.S.). Authors want to express their gratitude to the Turkish Meteorology General Directorate (MGM) for providing the database used in this study.
dc.identifier.doi10.1002/joc.70105
dc.identifier.issn0899-8418
dc.identifier.issn1097-0088
dc.identifier.issue14
dc.identifier.scopus2-s2.0-105014595116
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/joc.70105
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5428
dc.identifier.volume45
dc.identifier.wosWOS:001559315800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofInternational Journal of Climatology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectdrought
dc.subjectfast multi-output relevance vector regression
dc.subjectmulti station drought estimation
dc.subjectmulti-output estimation
dc.subjectstandardized precipitation evaporation index
dc.subjectstreamflow drought index
dc.titleNon-Linear Output Structure Learning: A Novel Multi-Target Technique for Multi-Station and Multi-Index Drought Modelling
dc.typeArticle

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