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Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning.

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|April 26, 2019
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Summary

A new GIS-based statistical approach effectively predicts groundwater nitrate levels, overcoming limitations of complex models. The Random Forest model, using hydrogeological units and land use data, offers a scalable solution for regional nitrate management.

Keywords:
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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Geographic Information Systems (GIS)

Background:

  • Reducing nitrate (NO3-) inputs to groundwater is crucial for meeting European Water Framework Directive standards.
  • Complex hydro-biogeochemical models are often limited by data availability and spatial resolution for large-scale management plans.

Purpose of the Study:

  • To develop a parsimonious GIS-based statistical approach for estimating spatial groundwater nitrate distribution.
  • To investigate the impact of different contributing area designs on statistical model performance.
  • To identify the most influential spatial predictors for groundwater nitrate concentration.

Main Methods:

  • A GIS-based statistical approach was employed, training models with point nitrate concentrations and spatial environmental data.
  • Four statistical models were compared: Multiple Linear Regression (MLR), Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Trees (BRT).
  • The performance of models was evaluated using various objective functions, and influential predictors were identified.

Main Results:

  • The Random Forest model demonstrated superior predictive performance (R² = 0.54) compared to other models.
  • Hydrogeological units, percentage of arable land, and nitrogen balance were identified as the most influential predictors.
  • A 1000m circular contributing area was found to be optimal for predictor relevance.

Conclusions:

  • The developed GIS-based statistical approach provides a viable alternative to complex models for regional groundwater nitrate prediction.
  • Utilizing exclusively spatially available predictors represents a significant advancement for regional-scale nitrate assessment.
  • This method supports the development of effective groundwater management plans for nitrate mitigation.