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Monitoring and Analysis of Groundwater Recharge Dynamic

  • 2025
  • OriginalPaper
  • Chapter
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Abstract

This chapter delves into the critical issue of groundwater depletion and its impact on cities like Nashik, India. It highlights the urgent need for accurate groundwater level predictions to mitigate water scarcity. The authors employ machine learning technologies, specifically SARIMA models, to analyze rainfall data and predict groundwater recharge rates. The methodology involves data collection, preprocessing, stationarity checks, and model training to ensure accurate predictions. The results include rainfall and groundwater level predictions for the next four years, with an R-square score of 0.75 for rainfall predictions. The chapter concludes with a discussion on the broader implications of these findings for sustainable water resource management, emphasizing the importance of understanding groundwater dynamics and recharge potential. This detailed analysis provides valuable insights for professionals seeking to address water scarcity and improve groundwater management practices.

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Title
Monitoring and Analysis of Groundwater Recharge Dynamic
Authors
Megha Jain
Mukund Madhav Sharma
Copyright Year
2025
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-96-9841-7_8
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