Analysis of Estimation of Soundness and Deterioration Factors of Sewage Pipes Using Machine Learning

In Japan, there are a massive number of sewage pipes buried in the ground. In order to operate sustainable sewerage systems, it is necessary to estimate the soundness of sewage pipes accurately and to conduct repairs and other measures according to the soundness of the pipes. In previous studies, statistical and machine learning methods have been used to estimate the soundness of sewage pipes, but all of these studies formulated the soundness of sewage pipes as a binary classification problem (e.g., good or poor). In contrast, this study attempted to predict the soundness of sewage pipes in more detail by setting up four classes of pipe soundness. Inspection data of sewage pipes in City A were used as training data, and XGBoost was used as the machine learning model. Machine learning models have a high prediction performance, but the uncertainty of the prediction basis is an issue. In this study, SHAP (Shapley additive explanations), an Explainable AI method, was used to interpret the model to clarify the influence of sewer pipe specifications (e.g., pipe age) and topographical specifications (e.g., annual precipitation) on the prediction, and to extract deterioration factors. By interpreting the model using SHAP, it was possible to quantify whether factors such as pipe age and pipe length have a positive or negative impact on the deterioration of sewage pipes. Previous studies using machine learning methods have not clarified whether factors have a positive or negative effect on deterioration. The knowledge on deterioration factors obtained in this study may provide useful information for the sustainable operation of sewage systems.