Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Turkiye

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dc.authorid0000-0001-9364-9738
dc.authorid0000-0002-4767-6660
dc.authorid0000-0003-0559-5261
dc.authorid0000-0003-4187-3812
dc.contributor.authorGul, Enes
dc.contributor.authorStaiou, Efthymia
dc.contributor.authorSafari, Mir Jafar Sadegh
dc.contributor.authorVaheddoost, Babak
dc.date.accessioned2026-02-12T21:05:17Z
dc.date.available2026-02-12T21:05:17Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Turkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R-2) and the Willmott Index (WI). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Turkiye.
dc.description.sponsorshipYasar University, BAP 095 project entitled Drought Assessment in Izmir District, Turkey
dc.description.sponsorshipThis study is supported by Yasar University, BAP 095 project entitled Drought Assessment in Izmir District, Turkey, under the coordination of the third author (M.J.S. Safari).
dc.identifier.doi10.3390/su151511568
dc.identifier.issn2071-1050
dc.identifier.issue15
dc.identifier.scopus2-s2.0-85167889664
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su151511568
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6867
dc.identifier.volume15
dc.identifier.wosWOS:001046441300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260212
dc.subjectboosting method
dc.subjectdrought modeling
dc.subjecthyperparameter optimization
dc.subjectstandard precipitation index
dc.titleEnhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Turkiye
dc.typeArticle

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