Groundwater vulnerability assessment in central Iran: Integration of GIS-based DRASTIC model and a machine learning approach

Keyphrases

• Agricultural Sector 50%
• Aquifer 50%
• Aquifer Media 50%
• Associated Health Risks 50%
• AUC Value 50%
• Central Iran 100%
• DRASTIC Method 50%
• DRASTIC Model 100%
• GIS-based 100%
• Groundwater 50%
• Groundwater Pollution 50%
• Groundwater Pollution Risk 50%
• Groundwater Quality 50%
• Groundwater Resources 50%
• Groundwater Vulnerability Assessment 100%
• Groundwater Vulnerability Map 50%
• Hydraulic Conductivity 50%
• Industrial Waste 50%
• Infiltration 50%
• Machine Learning Algorithms 50%
• Machine Learning Approach 100%
• Machine Learning Models 50%
• Model Validation 50%
• Model-driven Development 50%
• Moderate Pollution 50%
• Net Recharge 50%
• Pollution Risk 50%
• Predictive Power 50%
• Preventive Measures 50%
• R Software 50%
• Receiver Operating Characteristic Curve 50%
• RF Model 50%
• Risk Model 50%
• Sewage Waste 50%
• Soil Medium 50%
• SVM Algorithm 50%
• Training Data 50%
• Training Model 50%
• Urban Sewage 50%
• Vadose Zone 50%
• Validation Data 50%
• Vulnerability 50%
• Water Resources Vulnerability 50%
• Water Table Depth 50%

Earth and Planetary Sciences

• Aquifer Recharge 33%
• Development Model 33%
• Geographic Information Systems 100%
• Groundwater 33%
• Groundwater Pollution 66%
• Groundwater Resource 33%
• Groundwater Vulnerability Assessment 100%
• Hydraulic Conductivity 33%
• Industrial Waste 33%
• Iran 100%
• Machine Learning 100%
• Pollution Risk 66%
• Vadose Zone 33%
• Water Resource 33%
• Water Table 33%