The Association between Meteorological Drought and the State of the Groundwater Level in Bursa Turkey

dc.contributor.author Babak Vaheddoost
dc.contributor.author Babak Mohammadi
dc.contributor.author Mir Jafar Sadegh Safari
dc.contributor.author Vaheddoost, Babak
dc.contributor.author Safari, Mir Jafar Sadegh
dc.contributor.author Mohammadi, Babak
dc.date.accessioned 2025-10-06T17:49:20Z
dc.date.issued 2023
dc.description.abstract This study addressed the intricate interplay between meteorological droughts and groundwater level fluctuations in the vicinity of Mount Uludag in Bursa Turkey. To achieve this an exhaustive analysis encompassing monthly precipitation records and groundwater level data sourced from three meteorological stations and eight groundwater observation points spanning the period from 2007 to 2018 was performed. Subsequently this study employed the Standard Precipitation Index (SPI) and Standard Groundwater Level (SGL) metrics meticulously calculating the temporal extents of drought events for each respective time series. Following this a judicious application of both the Thiessen and Support Vector Machine (SVM) methodologies was undertaken to ascertain the optimal groundwater observation wells and their corresponding SGL durations aligning them with SPI durations tied to the selected meteorological stations. The SVM technique in particular excelled in the identification of the most pertinent observation wells. Additionally the Elman Neural Network (ENN) and its optimized version through the Firefly Algorithm (ENN-FA) demonstrated their prowess in accurately predicting SPI durations based on SGL durations. The results were favorable as evidenced by the commendable performance metrics of the Normalized Root Mean Square Error (NRMSE) the Nash–Sutcliffe Efficiency (NSE) the product of the coefficient of determination and the slope of the regression line (bR2) and the Kling–Gupta Efficiency (KGE). Consequently the favorable simulation results were construed as evidence supporting the presence of a discernible association between SGL and the duration of the SPI. As we substantiate the concordance between the temporal extent of meteorological droughts and the perturbations in groundwater levels this unmistakably underscores the fact that the historical fluctuations in groundwater levels within the region were predominantly attributable to climatic influences rather than being instigated by anthropogenic activities. Nevertheless it is imperative to underscore that this revelation should not be misconstrued as an endorsement of future heedless exploitation of groundwater resources. © 2024 Elsevier B.V. All rights reserved.
dc.description.sponsorship The authors wish to extend their gratitude to the State Hydraulic Works and the State Meteorological Services for generously providing the data utilized in this study.
dc.identifier.doi 10.3390/su152115675
dc.identifier.issn 20711050
dc.identifier.issn 2071-1050
dc.identifier.scopus 2-s2.0-85186224188
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186224188&doi=10.3390%2Fsu152115675&partnerID=40&md5=7308843cb6e7f0407896df864343bf4f
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/8385
dc.identifier.uri https://doi.org/10.3390/su152115675
dc.language.iso English
dc.publisher Multidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartof Sustainability
dc.rights info:eu-repo/semantics/openAccess
dc.source Sustainability (Switzerland)
dc.subject Drought Duration, Elman Neural Network, Firefly Algorithm, Groundwater Level, Support Vector Machine, Algorithm, Artificial Neural Network, Drought, Exploitation, Groundwater, Simulation, Support Vector Machine, Water Level, Bursa
dc.subject algorithm, artificial neural network, drought, exploitation, groundwater, simulation, support vector machine, water level, Bursa
dc.subject Groundwater Level
dc.subject Drought Duration
dc.subject Firefly Algorithm
dc.subject Support Vector Machine
dc.subject Elman Neural Network
dc.title The Association between Meteorological Drought and the State of the Groundwater Level in Bursa Turkey
dc.type Article
dspace.entity.type Publication
gdc.author.id Vaheddoost, Babak/0000-0002-4767-6660
gdc.author.id Mohammadi, Babak/0000-0001-8427-5965
gdc.author.id Safari, Mir Jafar Sadegh/0000-0003-0559-5261
gdc.author.scopusid 57195411533
gdc.author.scopusid 56047228600
gdc.author.scopusid 57113743700
gdc.author.wosid Safari, Mir Jafar Sadegh/A-4094-2019
gdc.author.wosid Mohammadi, Babak/JCO-4552-2023
gdc.author.wosid Vaheddoost, Babak/M-6824-2018
gdc.bip.impulseclass C4
gdc.bip.influenceclass C5
gdc.bip.popularityclass C4
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department
gdc.description.departmenttemp [Vaheddoost, Babak] Bursa Tech Univ, Dept Civil Engn, TR-16310 Bursa, Turkiye; [Mohammadi, Babak] Lund Univ, Dept Phys Geog & Ecosyst Sci, Solvegatan 12, S-22362 Lund, Sweden; [Safari, Mir Jafar Sadegh] Yasar Univ, Dept Civil Engn, TR-35100 Izmir, Turkiye
gdc.description.issue 21
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
gdc.description.startpage 15675
gdc.description.volume 15
gdc.description.woscitationindex Science Citation Index Expanded - Social Science Citation Index
gdc.identifier.openalex W4388408021
gdc.identifier.wos WOS:001099556300001
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gdc.oaire.influence 2.565654E-9
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gdc.oaire.keywords drought duration
gdc.oaire.keywords Elman neural network
gdc.oaire.keywords firefly algorithm
gdc.oaire.keywords groundwater level
gdc.oaire.keywords support vector machine
gdc.oaire.popularity 5.793259E-9
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gdc.oaire.sciencefields 0207 environmental engineering
gdc.oaire.sciencefields 02 engineering and technology
gdc.oaire.sciencefields 01 natural sciences
gdc.oaire.sciencefields 0105 earth and related environmental sciences
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gdc.virtual.author Safari, Mir Jafar Sadegh
gdc.wos.citedcount 5
person.identifier.scopus-author-id Vaheddoost- Babak (57113743700), Mohammadi- Babak (57195411533), Safari- Mir Jafar Sadegh (56047228600)
publicationissue.issueNumber 21
publicationvolume.volumeNumber 15
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