Logistic regression model for sinkhole susceptibility due to damaged sewer pipes

The occurrence of anthropogenic sinkholes in urban areas can lead to severe socioeconomic losses. A damaged underground sewer pipe is regarded as one of the primary causes of such a phenomenon. This study adopted the best subsets regression method to produce a logistic regression model that evaluates the susceptibility for sinkholes induced by damaged sewer pipes. The model was developed by analyzing the sewer pipe network as well as cases of sinkholes in Seoul, South Korea. Among numerous sewer pipe characteristics tested as explanatory variables, the length, age, elevation, burial depth, size, slope, and materials of the sewer pipe were found to influence the occurrence of sinkhole. The proposed model reasonably estimated the sinkhole susceptibility in the area studied, with an area value under the receiver-operating characteristics curve of 0.753. The proposed methodology will serve as a useful tool that can help local governments to choose a cavity inspection regime, and to prevent sinkholes induced by damaged sewer pipes.