Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping

Elham Hosseinzadeh, Sara Anamaghi, Massoud Behboudian () and Zahra Kalantari
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Elham Hosseinzadeh: Department of Civil Engineering, University of Tabriz, Tabriz 51666-16471, Iran
Sara Anamaghi: Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
Massoud Behboudian: Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH Royal Institute of Technology, 11428 Stockholm, Sweden
Zahra Kalantari: Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH Royal Institute of Technology, 11428 Stockholm, Sweden

Land, 2024, vol. 13, issue 3, 1-27

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 regression trees (CARTs), (2) boosted regression tree (BRT), (3) Bayesian linear regression (BLR), (4) support vector machine (SVM), (5) random forest (RF), (6) logistic regression (LogR), and (7) multiple linear regression (MLR), in generating LS susceptibility maps and predicting LS in two case studies (Semnan Plain and Kashmar Plain in Iran) with varying intrinsic characteristics and available data points. Multiple input variables (slope, aspect, groundwater drawdown, distance from the river, distance from the fault, lithology, land use, topographic wetness index (TWI), and normalized difference vegetation index (NDVI)), were used as predictors. BRT outperformed the other classification approaches in both case studies, with accuracy rates of 75% and 74% for Semnan and Kashmar plains, respectively. The MLR approach yielded a Mean Square Error (MSE) of 0.25 for Semnan plain and 0.32 for Kashmar plain. According to the BRT approach, the variables playing the most significant role in LS in Semnan Plain were groundwater drawdown (20.31%), distance from the river (17.11%), land use (14.98%), NDVI (12.75%), and lithology (11.93%). Moreover, the three most important factors in LS in Kashmar Plain were groundwater drawdown (35.31%), distance from the river (23.1%), and land use (12.98%). The results suggest that the BRT method is not significantly affected by data set size, but increasing the number of training set data points in MLR results in a decreased error rate.

Keywords: land subsidence modeling; classification; machine learning algorithms; Semnan plain; Kashmar Plain (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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