Improving the Accuracy of Groundwater Level Forecasting by Coupling Ensemble Machine Learning Model and Coronavirus Herd Immunity Optimizer

Basit Öğe Kaydı
dc.contributor.authorAhmed M. Saqr
dc.contributor.authorVeysi Kartal
dc.contributor.authorErkan Karakoyun
dc.contributor.authorMahmoud E. Abd-Elmaboud
dc.date.accessioned2025-05-12T12:09:05Z
dc.date.available2025-05-12T12:09:05Z
dc.date.issued2025-05-03
dc.departmentFakülteler, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractGroundwater levels are under severe pressure globally due to over-extraction, pollution, and climate change necessitating continuous monitoring for sustainable aquifer management. This study introduces a novel ensemble machine learning (En) model that integrates shallow and deep machine learning (ML) models, optimized through the coronavirus herd immunity optimizer (CHIO), for accurate groundwater level forecasting. This En model was applied to the Ergene River Basin, Türkiye, a region facing severe groundwater depletion and contamination due to intensive agricultural and industrial activities. Groundwater level data spanning 1966 to 2023 on a weekly basis from four wells were used, split into 70% for training and 30% for testing under short- and long-term scenarios. Using the partial autocorrelation function and gamma test the best lag numbers were determined for input data, reflecting aquifer heterogeneity. Score analysis, supported by statistical metrics such as the coefficient of determination (R²) and root mean square error (RMSE), was employed alongside visual aids to assess the developed En model performance. Results demonstrated that deep ML models outperformed shallow ML models achieving R² ~ 0.99 and RMSE ~ 0.5 m. The developed En model outperformed all individual ML models, with score values exceeding 200, and its predictions closely aligned with measured water levels during both testing phases. The findings underscored the developed En model’s contribution to achieving sustainable development goals (SDGs) by enhancing water-use efficiency and addressing environmental, economic, and social sustainability challenges. The proposed approach offers a reliable and adaptable solution for groundwater level forecasting, applicable to other aquifers worldwide.
dc.identifier.citationSaqr, A. M., Kartal, V., Karakoyun, E., & Abd-Elmaboud, M. E. (2025). Improving the accuracy of groundwater level forecasting by coupling ensemble machine learning model and Coronavirus Herd Immunity Optimizer. Water Resources Management, 1-28.
dc.identifier.doi10.1007/s11269-025-04210-w
dc.identifier.issn0920-4741
dc.identifier.issn1573-1650
dc.identifier.scopus2-s2.0-105004049256
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s11269-025-04210-w
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8651
dc.identifier.wos001481867800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorKartal, Veysi
dc.institutionauthorid0000-0003-4671-1281
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofWater Resources Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCoronavirus herd immunity optimizer
dc.subjectDeep learning
dc.subjectEnsemble model
dc.subjectGroundwater level forecasting
dc.subjectSustainable development goals
dc.subjectTime series analysis
dc.titleImproving the Accuracy of Groundwater Level Forecasting by Coupling Ensemble Machine Learning Model and Coronavirus Herd Immunity Optimizer
dc.typejournal-article

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Veysi-Kartal-2025.pdf
Boyut:
5.24 MB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
WOS İndeksli Yayınlar Koleksiyonu