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Integrating machine learning models to map springshed potential zones using key environmental variables in

Rakesh Kadaverugu1,2, Asha Dhole3,4, P V Nidheesh3,4

  • 1CSIR-National Environmental Engineering Research Institute, Nagpur, 440020, Maharashtra, India. r_kadaverugu@neeri.res.in.

Environmental Monitoring and Assessment
|May 20, 2026
PubMed
Summary

Machine learning models predict potential spring zones in the Indian Himalayas, identifying key factors like elevation and precipitation. This framework aids in managing and restoring vital water resources in the region.

Keywords:
Ensemble modelGoogle Earth EngineIndian Himalayan RegionKishtwarMachine learningSpringshed assessment

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Area of Science:

  • Geosciences
  • Environmental Science
  • Data Science

Background:

  • High-altitude springs in the Indian Himalayas are crucial water sources but face depletion due to climate change and human activities.
  • Accurate prediction of spring occurrence zones is vital for effective springshed management and conservation efforts.

Purpose of the Study:

  • To develop and integrate machine learning models for predicting potential spring occurrence zones in the Kishtwar region.
  • To identify key environmental and climatic predictors influencing spring distribution in the study area.

Main Methods:

  • Utilized 42 geotagged spring locations and pseudo-absence data for model training and testing (80:20 split).
  • Employed predictor variables including soil moisture, elevation, slope, Enhanced Vegetation Index (EVI), precipitation, and hydraulic conductivity derived from remote sensing.
  • Trained Random Forest, MaxEnt, and CART models, integrating them via linear regression to minimize bias.

Main Results:

  • Elevation, precipitation, and soil parameters were identified as the most significant predictors of spring occurrence.
  • The study delineated 815.6 km² as potential and 467.3 km² as high-probability spring zones.
  • The ensemble model achieved high discriminatory performance with an Area Under the Curve (AUC) up to 0.99.

Conclusions:

  • The integrated machine learning framework provides a scalable approach for Himalayan springshed management.
  • Outputs serve as screening-level prioritization for data-driven restoration efforts, despite limitations of the observed spring data.
  • Advanced geospatial and machine learning integration is demonstrated as a powerful tool for hydrological resource management.