Relative performance of different data mining techniques for nitrate concentration and load estimation in different type of watersheds

Keyphrases

• Relative Performance 100%
• Data Mining Techniques 100%
• Load Estimation 100%
• Nitrate Concentration 100%
• Nitrate Load 100%
• Concentration Estimation 100%
• Reduced Error Pruning Tree 75%
• K-nearest 50%
• Multilayer Perceptron 50%
• Agricultural Watershed 50%
• Forested Watershed 50%
• Water Quality Analysis 50%
• Clustering Methods 25%
• Simulation Method 25%
• Midwestern United States 25%
• Algorithmic Approach 25%
• Linear Regression 25%
• Modeling Method 25%
• Accurate Modeling 25%
• Tree-based Methods 25%
• Land Use 25%
• Efficient Modeling 25%
• Water Quality 25%
• Classification Methods 25%
• Random Forest 25%
• Land Use Practices 25%
• Land Type 25%
• Relative Absolute Error 25%
• Classification Tree Method 25%
• Water Quality Prediction 25%
• Nitrogen Pollutants 25%

Earth and Planetary Sciences

• Pruning 100%
• Self Organizing Systems 66%
• Land Use 66%
• United States 33%
• Land Type 33%

Agricultural and Biological Sciences

• Watersheds 100%
• Water Quality Analysis 33%
• Land Use 33%
• Agricultural Watersheds 16%
• Forested Watersheds 16%