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

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dc.authoridGUL, ENES/0000-0001-9364-9738
dc.authoridSafari, Mir Jafar Sadegh/0000-0003-0559-5261
dc.authoridVaheddoost, Babak/0000-0002-4767-6660
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.authorwosidSafari, Mir Jafar Sadegh/A-4094-2019
dc.authorwosidVaheddoost, Babak/M-6824-2018
dc.contributor.authorGul, Enes
dc.contributor.authorStaiou, Efthymia
dc.contributor.authorSafari, Mir Jafar Sadegh
dc.contributor.authorVaheddoost, Babak
dc.date.accessioned2024-08-04T20:54:37Z
dc.date.available2024-08-04T20:54:37Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
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.en_US
dc.description.sponsorshipYasar University, BAP 095 project entitled Drought Assessment in Izmir District, Turkeyen_US
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).en_US
dc.identifier.doi10.3390/su151511568
dc.identifier.issn2071-1050
dc.identifier.issue15en_US
dc.identifier.scopus2-s2.0-85167889664en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/su151511568
dc.identifier.urihttps://hdl.handle.net/11616/101515
dc.identifier.volume15en_US
dc.identifier.wosWOS:001046441300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectboosting methoden_US
dc.subjectdrought modelingen_US
dc.subjecthyperparameter optimizationen_US
dc.subjectstandard precipitation indexen_US
dc.titleEnhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Turkiyeen_US
dc.typeArticleen_US

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