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Susceptibility Assessment of Groundwater Nitrate Contamination Using an Ensemble Machine Learning Approach.

Farzaneh Sajedi Hosseini, Bahram Choubin1, Mehdi Bagheri-Gavkosh2

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This summary is machine-generated.

This study maps groundwater nitrate pollution susceptibility using novel machine learning models. The weighted subspace random forest (WSRF) model demonstrated superior performance, aiding water resource protection strategies.

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Data Science

Background:

  • Groundwater pollution susceptibility mapping is crucial for water resource management, particularly in data-limited areas.
  • Assessing groundwater nitrate susceptibility often involves analyzing various combinations of explanatory variables.

Purpose of the Study:

  • To evaluate novel machine learning models, weighted subspace random forest (WSRF) and generalized additive model using LOESS (GAMLOESS), for groundwater nitrate pollution susceptibility mapping.
  • To compare the performance of these new models against established methods like K-nearest neighbors (KKNN) and random forest (RF).
  • To identify the optimal combination of input variables for accurate susceptibility mapping.

Main Methods:

  • Application of WSRF and GAMLOESS machine learning models.
  • Comparison with KKNN and RF models.
  • K-fold cross-validation to determine the best input variable combination.
  • Evaluation using metrics such as accuracy, kappa, precision, false alarm ratio, and critical success index.

Main Results:

  • The combination of precipitation, groundwater level, and lithology proved to be the most effective input variables among 16 tested combinations.
  • The WSRF model exhibited superior performance, achieving an accuracy of 0.87, kappa of 0.73, precision of 0.92, false alarm ratio of 0.08, and critical success index of 0.75.
  • The novel WSRF ensemble approach outperformed other tested models.

Conclusions:

  • The WSRF model is highly effective for groundwater nitrate susceptibility mapping, especially with limited data.
  • The findings provide a valuable tool for developing effective groundwater pollution prevention and protection strategies.
  • Parsimonious approaches using machine learning can significantly enhance water resource planning in data-scarce regions.