Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques

Keyphrases

• Systems-based 100%
• Spatial Distribution 100%
• Aquifer System 100%
• Arsenic 100%
• Machine Learning Techniques 100%
• Heavy Metals 100%
• Heavy Metal Concentration 100%
• Physicochemical Parameters 100%
• Shallow Aquifer 100%
• Feature Selection 50%
• Prediction Accuracy 50%
• Model Performance 50%
• Laboratory Work 50%
• Modeling Techniques 50%
• Water Samples 50%
• Predictive Models 50%
• Random Forest 50%
• Effective Feature 50%
• Pollution Episodes 50%
• Model Selection Uncertainty 50%
• Effective Model 50%
• Analytical Process 50%
• Four-step Model 50%
• Sampling Process 50%
• Manganese 50%
• Modeling Process 50%
• Predictive Uncertainty 50%
• Artificial Intelligence Algorithm 50%
• Time Work 50%
• Iron-manganese 50%
• Explainability 50%
• Fast Modeling 50%
• Water Parameters 50%
• Nutrient Distribution 50%
• Heavy Metals in Groundwater 50%
• Model Interpretability 50%
• Selection Uncertainty 50%
• Integrated Assessment Framework 50%

Earth and Planetary Sciences

• Machine Learning 100%
• Heavy Metal 100%
• Spatial Distribution 50%
• Salinity 25%
• Water Sampling 25%
• Artificial Intelligence 25%
• Iron 25%

Chemical Engineering

• Learning System 100%
• Heavy Metal 100%
• Artificial Intelligence 25%