Evaluating machine learning performance in predicting sodium adsorption ratio for sustainable soil-water management in the eastern Mediterranean
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View abstract on PubMed
Summary
This summary is machine-generated.This study used machine learning models to predict soil salinity in the eastern Mediterranean, finding NuSVR most accurate for soil health management and sustainable agriculture. Key factors influencing salinity were identified for regional application.
Area Of Science
- Agricultural Science
- Environmental Science
- Data Science
Background
- Soil salinization is a major threat to global food security and sustainable agriculture.
- Monitoring soil salinity and its drivers at a regional scale is crucial for effective management.
Purpose Of The Study
- To evaluate machine learning (ML) models for predicting soil salinity using the Sodium Adsorption Ratio (SAR) in the eastern Mediterranean.
- To identify key environmental factors influencing SAR using SHapely Additive exPlanations (SHAP).
Main Methods
- Four ML models (Random Forest, Nu Support Vector Regression, Artificial Neural Network-Multi Layer Perceptron, Gradient Boosting Regression) were assessed.
- Recursive Feature Elimination (RFE) was used for feature selection.
- SHapely Additive exPlanations (SHAP) were employed for sensitivity analysis.
Main Results
- Nu Support Vector Regression (NuSVR) demonstrated superior performance in SAR prediction during training and testing.
- The model using seven selected features (Scenario 2) achieved high prediction accuracy.
- Cation Exchange Capacity (CEC), Ca+2, Mg+2, and Na+ were identified as the most influential variables for SAR prediction.
Conclusions
- Machine learning, particularly NuSVR, offers a powerful tool for accurate soil salinity prediction.
- Understanding influential factors like CEC and specific cations is vital for targeted soil management strategies.
- The findings provide valuable insights for enhancing agricultural soil management in the eastern Mediterranean region.
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