Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping
Elham Hosseinzadeh,
Sara Anamaghi,
Massoud Behboudian
et al.
Abstract:Land subsidence (LS) due to natural and human-driven forces (e.g., earthquakes and overexploitation of groundwater) has detrimental and irreversible impacts on the environmental, economic, and social aspects of human life. Thus, LS hazard mapping, monitoring, and prediction are important for scientists and decision-makers. This study evaluated the performance of seven machine learning approaches (MLAs), comprising six classification approaches and one regression approach, namely (1) classification and regressi… Show more
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