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Mapping specific groundwater nitrate concentrations from spatial data using machine learning: A case study of

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Machine learning models accurately predict groundwater nitrate levels, identifying key factors influencing pollution. This aids in safeguarding vital water resources and protecting human health from contamination.

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Data Science

Background:

  • Groundwater is a critical resource, but high nitrate levels pose significant human health risks.
  • Accurate mapping of groundwater nitrate distribution is essential for environmental management.

Purpose of the Study:

  • To develop and compare machine learning models for predicting groundwater nitrate concentration distribution.
  • To identify the primary environmental and anthropogenic factors influencing nitrate levels in groundwater.

Main Methods:

  • Four machine learning models (Gradient Boosting Regression, Random Forest Regression, Extreme Gradient Boosting Regression, Adaptive Boosting Regression) were employed.
  • Models integrated spatial environmental data, including topography, remote sensing, hydrogeology, hydrology, climate, nitrate input, and socio-economic factors, using a 500m contributing area.
  • 595 groundwater samples were utilized for model training and validation.

Main Results:

  • The Gradient Boosting Regression model demonstrated superior performance (R² = 0.627) in predicting nitrate concentrations.
  • A high-resolution map revealed most areas have nitrate levels below 1 mg/L, with higher concentrations (>10 mg/L) in specific urban and karst valley regions.
  • Hydrogeological conditions, soil properties, nitrogen inputs, and arable land percentage were identified as key influential factors.

Conclusions:

  • Machine learning, particularly Gradient Boosting Regression, offers an effective approach for spatial prediction of groundwater nitrate concentrations.
  • Findings provide crucial decision-making support for groundwater pollution control in Chongqing, especially for areas reliant on groundwater.
  • This study represents a significant advancement in applying spatial methods for groundwater quality assessment.