Gully erosion susceptibility mapping using the stacking ensemble machine learning method

Keyphrases

• Deep Neural Network 100%
• Ensemble Machine Learning 100%
• Gully Erosion 100%
• Stacking Ensemble 100%
• Gully Erosion Susceptibility 100%
• Environmental Factors 66%
• K-nearest 66%
• Random Forest 66%
• Metamodel 66%
• Gradient Boosting Machine 66%
• Illinois 33%
• Resource Allocation 33%
• Mutual Information 33%
• Politicians 33%
• Performance Evaluation 33%
• Prevention Efforts 33%
• Prediction Accuracy 33%
• Correlation Coefficient 33%
• Large Channel 33%
• Feature Selection 33%
• Water Resources 33%
• Agricultural Land 33%
• Remediate 33%
• Sustainable Development Goals 33%
• Land Slope 33%
• Soil Erosion 33%
• Soil Resources 33%
• Land Managers 33%
• Land Management 33%
• Ensemble Model 33%
• Gully 33%
• Collinearity 33%
• Mitigation Efforts 33%
• Preventive Behavior 33%
• Gully Formation 33%
• Agricultural Machinery 33%
• Bayesian Optimization 33%
• Socio-economic Losses 33%
• Jefferson County 33%
• K-fold Cross-validation 33%
• Cramer's V 33%
• Ensemble Learning Techniques 33%
• Stacking Ensemble Learning 33%
• Stacking Model 33%
• Gully Development 33%

Earth and Planetary Sciences

• Stacking 100%
• Machine Learning 100%
• Gully Erosion 100%
• United States of America 16%
• Illinois 16%
• Socioeconomics 16%
• Correlation Coefficient 16%
• Water Resource 16%
• Farmland 16%
• Vegetation 16%
• Soil Erosion 16%
• Land Management 16%
• Soil Resource 16%
• Action Plan 16%
• Sustainable Development Goals 16%
• Agricultural Equipment 16%
• Bayesian Optimization 16%