Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/5606
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dc.contributor.authorPanda, Shubhshree-
dc.contributor.authorSahoo, Sanat Nalini-
dc.date.accessioned2026-01-20T09:55:27Z-
dc.date.available2026-01-20T09:55:27Z-
dc.date.issued2025-12-
dc.identifier.citationInternational Conference On Wetland and Water Resource for Sustainable Development (WET-WAR), NIT, Patna, 29-31 December 2025en_US
dc.identifier.urihttp://hdl.handle.net/2080/5606-
dc.descriptionCopyright belongs to the proceeding publisher.en_US
dc.description.abstractGroundwater is a vital resource for the coastal districts of Odisha, where rainfall plays a crucial role in groundwater recharge. Therefore, the present study assesses the impact of rainfall on groundwater levels for the reduction of the issue of seawater intrusion. The statistical approach and regression-based study were conducted for the assessment of rainfall on the groundwater level. Groundwater level data from observation wells and corresponding rainfall records were analyzed to establish trends and correlations. The Mann-Kendall test was employed to detect trends in groundwater fluctuations, while regression models, including multiple linear regression (MLR) was used to quantify the relationship between rainfall and groundwater response. The findings indicate a significant dependence of groundwater levels on seasonal rainfall variations, with recharge observed in certain regions in all the seasons except the pre-monsoon season due to precipitation deficit. The relationship equation generated through MLR shows the change in GWL with rainfall. The study also highlights spatial variability in groundwater response due to rainfall. The results provide insights for sustainable groundwater management, aiding policymakers in developing effective water resource strategies in coastal regions vulnerable to climate change.en_US
dc.subjectGroundwater levelsen_US
dc.subjectStatistical analysisen_US
dc.subjectRegression modelingen_US
dc.titleAssessing the Impact of Rainfall on Groundwater Levels: A Statistical and Regression-Based Approachen_US
dc.typeArticleen_US
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