Karst Collapse Risk Zonation and Evaluation in Wuhan, China Based on Analytic Hierarchy Process, Logistic Regression, and InSAR Angular Distortion Approaches

Abstract

The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium- and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP- and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation.

The Analyses of Land Use and Prevention in High-Density Main Urban Areas under the Constraint of Karst Ground Subsidence: Study of Wuhan City, China

“…Many complex and intertwined factors contribute to the subsidence of karst ground, presenting a complex nonlinear dynamic characteristic among various influencing factors. However, there is also an apparent correlation and classification [23]. The traditional qualitative evaluation method cannot comprehensively reflect the multi-factor effects of KGS.…”

“…However, there is still a lack of analysis from both the perspective of geological, environmental conditions and the intensity of large-scale engineering construction activities regarding the restrictive effect of karst ground collapse on land use in central urban areas (small-scale areas with high-intensity human activities). Similarly, insufficient research identifies factors causing different regional karst ground collapses and proposes targeted control measures for land development and utilization in central urban areas [22][23][24].…”

Analysis of Differences in Comments of Internet Users under Hot Internet Events: The Case of the Nuclear Sewage Discharge Incident in Japan

“…In order to solve the problem of this paper, multiple levels are selected, in which the target level is set as the Internet platform where the network public opinion is located. As the Internet platform as the gathering place of Internet users, the users held by different platforms also have different properties [20]. Among them, when conducting Internet user research, large platforms tend to have richer user groups, and with reference to the current indexes related to China's internal and international Internet, this paper chooses large entertainment or social platforms such as BILIBILI, Youtube, Tiktok, Douyin, Twitter (X) and Weibo.…”

Risk zonation of submarine geological hazards in the Chengdao area of the Yellow River subaqueous delta

“…The AHP is a qualitative and quantitative multi-objective decision analysis method, and it is suitable for situations where the target structure is complex and the necessary data are lacking. It can organize various factors in complex problems in a certain interrelated orderly level and can provide a comparable quantitative basis for analysis, decision-making, prediction, or development control (Subedi et al, 2019;Hu et al, 2021;Wei et al, 2021). Therefore, based on the AHP, in this paper, an evaluation index hierarchy model is established and weight calculations are conducted to make the zonation results more scientific and accurate.…”