Spatial distribution modeling of customer complaints using machine learning for indoor water leakage management

Abstract

Customer complaints reflect the needs of citizens and provide valuable information for the efficient management of urban problems. Indoor water leakage management is required to achieve a sustainable water infrastructure and urban development. In this study, a machine learning (ML)-based modeling framework was developed for predicting the spatial distribution of customer complaints about indoor water leakage in the downtown area of Daegu Metropolitan City, South Korea. Two ML algorithms (XGBoost and LightGBM) were used with six resampling methods (e.g., undersampling, oversampling, and hybrid sampling) to compare the prediction performances. The combination of LightGBM and hybrid sampling showed the highest prediction performance. Post hoc analysis using Shapley Additive Explanations indicated that, among the various input features, the land cover type, building and water infrastructure characteristics were of primary importance. High-resolution gridded mapping clearly revealed the spatial pattern of complaint probabilities. These results provide a decision support tool for indoor water leakage management. The proposed modeling framework encompasses data preprocessing and integration, prediction, interpretation, and spatial mapping, and it is applicable to a wide variety of urban problems that require in-depth analysis of their spatial characteristics.