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July 29, 2026Irrigation and Drainage

Spatio‐Temporal Groundwater Vulnerability Assessment in the Paler Watershed Using GIS‐Based DRASTIC and Multialgorithm Machine Learning Approaches

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Authors

DJD. JawaharlalRSR K SinghCSC. D. Singh

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Overview

Randomized trial evaluates groundwater vulnerability in the Paler watershed, suggesting effective management strategies.

Key Points

  • This research aims to assess groundwater vulnerability using a combined GIS and machine learning approach.
  • GIS-based DRASTIC model utilized to evaluate hydrogeological characteristics
  • Five machine learning algorithms compared for forecast accuracy over 15 years
  • Data collected from 104 observation wells in relation to land use datasets from 2017 to 2024
  • SVM and XG Boost models achieved R2 values between 0.76 and 0.81
  • High-vulnerability zones increased from 35% in 2017 to 43% in 2024
  • SVM-predicted vulnerability indices showed strong correlation with observed nitrate concentrations (r = 0.82, p < 0.001)

Cite This Study

Jawaharlal et al. (2026) studied this question.

synapsesocial.com/papers/6a69a2a3c8da07d9defa6582https://doi.org/10.1002/ird.70191
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