Signature kernel ridge regression time series model: A novel approach for hydrological drought modeling using multi-station meteorological drought information

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dc.authorid0000-0002-4767-6660
dc.authorid0000-0003-0559-5261
dc.contributor.authorSafari, Mir Jafar Sadegh
dc.contributor.authorArashloo, Shervin Rahimzadeh
dc.contributor.authorVaheddoost, Babak
dc.date.accessioned2026-02-08T15:15:08Z
dc.date.available2026-02-08T15:15:08Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn the context of growing environmental challenges and the need for sustainable water resource management, hydrological drought prediction has gained prominence as a critical issue. Existing artificial intelligence and time series-based models for hydrological drought indices have traditionally been established using streamflow data. This study gives a significant progress in hydrological drought modeling through the introduction of the Signature Kernel Ridge Regression (SKRR) time series model. Instead of directly using rainfall and runoff data to develop a rainfall-runoff (RR) model, the Standardized Precipitation Evapotranspiration Index (SPEI) values in neighbor meteorological stations serve as inputs for estimating the Streamflow Drought Index (SDI) in target hydrometric stations, considering the 3-, 6-, and 12-month moving average time windows. The objective of this study is to enhance hydrological drought modeling by integrating soft computing techniques that effectively handle multivariate and irregular time series. The efficacy of the SKRR is compared with the well-established Generalized Regression Neural Network (GRNN), Random Forest (RF), and Auto Regressive Integrated Moving Average model with eXogenous input (ARIMAX). The findings indicate that SKRR is capable of precisely estimating SDI in three hydrometric stations using meteorological drought information from 14 stations, outperforming the GRNN, RF and ARIMAX models. The enhanced performance of the SKRR time series model stems from the utilization of a new and effective signature kernel which can be utilized for the study of irregularly sampled, multivariate time series in addition to be applicable to time series of different temporal spans while being a positive-definite kernel, facilitating usage in the Hilbert space. The novel drought based-RR model established by SKRR utilized various external stations' meteorological drought indices to compute the hydrological drought indices in target stations not only enhances the modeling capability but also progress our understanding of drought dynamics by showcasing the power of soft computing in handling environmental
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 coor-dination of the first author (M.J.S. Safari) . Authors want to express their gratitude to the Turkish Meteorology General Directorate (MGM) for providing the database used in this study.
dc.identifier.doi10.1016/j.asoc.2025.113343
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-105006701000
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2025.113343
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5626
dc.identifier.volume178
dc.identifier.wosWOS:001501108600004
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofApplied Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectDrought
dc.subjectSignature Kernel Ridge Regression
dc.subjectStandardized Precipitation Evapotranspiration
dc.subjectIndex
dc.subjectStreamflow Drought Index
dc.titleSignature kernel ridge regression time series model: A novel approach for hydrological drought modeling using multi-station meteorological drought information
dc.typeArticle

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