Publication:
Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India

Simple item page
dc.citedby5
dc.contributor.authorHalder K.en_US
dc.contributor.authorSrivastava A.K.en_US
dc.contributor.authorGhosh A.en_US
dc.contributor.authorNabik R.en_US
dc.contributor.authorPan S.en_US
dc.contributor.authorChatterjee U.en_US
dc.contributor.authorBisai D.en_US
dc.contributor.authorPal S.C.en_US
dc.contributor.authorZeng W.en_US
dc.contributor.authorEwert F.en_US
dc.contributor.authorGaiser T.en_US
dc.contributor.authorPande C.B.en_US
dc.contributor.authorIslam A.R.M.T.en_US
dc.contributor.authorAlam E.en_US
dc.contributor.authorIslam M.K.en_US
dc.contributor.authorid58980024500en_US
dc.contributor.authorid55456006700en_US
dc.contributor.authorid58772106800en_US
dc.contributor.authorid59311142600en_US
dc.contributor.authorid57208260443en_US
dc.contributor.authorid57210184276en_US
dc.contributor.authorid57194184241en_US
dc.contributor.authorid57208776491en_US
dc.contributor.authorid55273339300en_US
dc.contributor.authorid6604055290en_US
dc.contributor.authorid6602107359en_US
dc.contributor.authorid57193547008en_US
dc.contributor.authorid57218543677en_US
dc.contributor.authorid37004349000en_US
dc.contributor.authorid57208752044en_US
dc.date.accessioned2025-03-03T07:41:41Z
dc.date.available2025-03-03T07:41:41Z
dc.date.issued2024
dc.description.abstractGroundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond. ? The Author(s) 2024.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo155
dc.identifier.doi10.1186/s12302-024-00981-y
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85202974695
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85202974695&doi=10.1186%2fs12302-024-00981-y&partnerID=40&md5=796e28c71dcccdd2764cbcf77463002f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36247
dc.identifier.volume36
dc.publisherSpringeren_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleEnvironmental Sciences Europe
dc.subjectBankura
dc.subjectIndia
dc.subjectWest Bengal
dc.subjectDeforestation
dc.subjectFertilizers
dc.subjectRandom errors
dc.subjectBankura district
dc.subjectBoosting ensembles
dc.subjectF1 scores
dc.subjectGradient boosting
dc.subjectGroundwater potentials
dc.subjectMachine learning techniques
dc.subjectManagement practises
dc.subjectPerformance
dc.subjectRandom forests
dc.subjectVoting ensemble
dc.subjectdrinking water
dc.subjectdrought
dc.subjectensemble forecasting
dc.subjectgroundwater
dc.subjectgroundwater resource
dc.subjecthydrogeology
dc.subjectmachine learning
dc.subjectmanagement practice
dc.subjectmapping method
dc.subjecttraining
dc.subjectAdaptive boosting
dc.titleApplication of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern Indiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS