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 | |
| gdc.index.type | Scopus | |
| gdc.index.type | WoS | |
| gdc.oaire.accesstype | GOLD | |
| gdc.oaire.diamondjournal | false | |
| gdc.oaire.impulse | 5.0 | |
| gdc.oaire.influence | 2.565654E-9 | |
| gdc.oaire.isgreen | false | |
| 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 | |
| gdc.oaire.publicfunded | false | |
| 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 | |
| gdc.openalex.collaboration | International | |
| gdc.openalex.fwci | 0.7993 | |
| gdc.openalex.normalizedpercentile | 0.71 | |
| gdc.opencitations.count | 4 | |
| gdc.plumx.mendeley | 11 | |
| gdc.plumx.scopuscites | 5 | |
| gdc.scopus.citedcount | 5 | |
| 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 | |
| relation.isAuthorOfPublication | 08e59673-4869-4344-94da-1823665e239d | |
| relation.isAuthorOfPublication.latestForDiscovery | 08e59673-4869-4344-94da-1823665e239d | |
| relation.isOrgUnitOfPublication | ac5ddece-c76d-476d-ab30-e4d3029dee37 | |
| relation.isOrgUnitOfPublication.latestForDiscovery | ac5ddece-c76d-476d-ab30-e4d3029dee37 |
Files
Original bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- sustainability-15-15675-v2.pdf
- Size:
- 1.97 MB
- Format:
- Adobe Portable Document Format
