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

dc.contributor.author Enes Gul
dc.contributor.author Efthymia Staiou
dc.contributor.author Mir Jafar Sadegh Safari
dc.contributor.author Babak Vaheddoost
dc.date AUG
dc.date.accessioned 2025-10-06T16:22:05Z
dc.date.issued 2023
dc.description.abstract The 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.identifier.doi 10.3390/su151511568
dc.identifier.issn 2071-1050
dc.identifier.uri http://dx.doi.org/10.3390/su151511568
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/7218
dc.language.iso English
dc.publisher MDPI
dc.relation.ispartof Sustainability
dc.source SUSTAINABILITY
dc.subject boosting method, drought modeling, hyperparameter optimization, standard precipitation index
dc.subject NEURAL-NETWORK, PRECIPITATION, MACHINE, SYSTEM, SPI
dc.title Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region- Turkiye
dc.type Article
dspace.entity.type Publication
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gdc.bip.popularityclass C4
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.startpage 11568
gdc.description.volume 15
gdc.identifier.openalex W4385358529
gdc.index.type WoS
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gdc.oaire.diamondjournal false
gdc.oaire.impulse 19.0
gdc.oaire.influence 2.939878E-9
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gdc.oaire.keywords boosting method; drought modeling; hyperparameter optimization; standard precipitation index
gdc.oaire.keywords drought modeling
gdc.oaire.keywords standard precipitation index
gdc.oaire.keywords hyperparameter optimization
gdc.oaire.keywords boosting method
gdc.oaire.popularity 1.5677232E-8
gdc.oaire.publicfunded false
gdc.openalex.collaboration National
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gdc.opencitations.count 13
gdc.plumx.mendeley 32
gdc.plumx.newscount 1
gdc.plumx.scopuscites 21
gdc.virtual.author Safari, Mir Jafar Sadegh
person.identifier.orcid Safari- Mir Jafar Sadegh/0000-0003-0559-5261, STAIOU- EFTHYMIA/0000-0003-4187-3812, GUL- ENES/0000-0001-9364-9738, Vaheddoost- Babak/0000-0002-4767-6660,
project.funder.name Yasar University- BAP 095 project entitled Drought Assessment in Izmir District- Turkey
publicationissue.issueNumber 15
publicationvolume.volumeNumber 15
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