Abstract
In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.
Highlights
Land subsidence is one of the land degradation features usually occur due to the diversity of natural or anthropic effects that cause a change in the environment and have social and economic effects [1]
The results of this study indicated that the distance to lineament is the most factor for land subsidence occurrenceoccurrence which is inwhich agreement with the finding offinding
The results of specificity indicated that the Bayesian Logistic Regression (BLR) algorithm had the highest specificity value (0.882) based on the training phase; while, the BLR and Support Vector Machine (SVM) algorithms had the highest value of specificity (0.857) based on the validation dataset
Summary
Land subsidence is one of the land degradation features usually occur due to the diversity of natural or anthropic effects that cause a change in the environment and have social and economic effects [1]. Many land subsidence have occurred globally because of various reasons such as mining, dissolution of limestone, extraction of groundwater and natural gas, earthquake [2,3,4]. The land subsidence forms over a period of time due to overload above voids such as underground mining [5,6]. In South Korea, many land subsidence have occurred due to coal mining in the 1960s and 1970s since the coal mining was playing an important role in the industry. The abandoned mines did not decrease the environmental destructions including land subsidence and water pollution and their risks were increased [8]. The underground land subsidence can create damage to surface structures, including house, building, railroad and roads, as well as human injury [5]. Since ground recovery after occurrence of a land subsidence is a challenge and their rehabilitation is costly [6,9], cautionary operations and proper strategies for land subsidence are critical
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