The pressure on water infrastructure has increased due to an increase in the number of aging water pipes. Aging pipes are prone to failure, causing significant financial losses and service disruptions. The increasing number of aged pipes and limited budget for pipe rehabilitation or replacement necessitates water infrastructure asset management to ensure sustainable water supply services. In this study, contingent valuation was used to estimate the willingness to pay (WTP) and value improved water supply services through the implementation of asset management. To estimate the WTP at the individual and county levels, we performed a nationwide survey including eight provinces and 24 counties/cities with distinct water supply service performances. At the individual level, the median WTP estimated using the double-bounded dichotomous choice model was 249.50 KRW/month (0.22 USD/month). The results showed that high-level satisfaction of customers with water supply services and positive price perception of water bills resulted in a high WTP. At the county level, decreasing water supply service performances were associated with a low WTP, indicating that proper interventions by local utilities are required to achieve sustainable water supply services. Our results provide a quantitative basis for decision-making in implementation of water infrastructure asset management.

This study suggests a method for calculating the benefits of water pipe renewal based on an estimate of the water supply suspension risk. The proposed method based on five benefit items is more direct and specific than other benefit estimation methods. In addition, a methodology evaluating the economics of pipe renewal based on pipe failure rate is proposed for estimating the optimal renewal point from an economic perspective. By estimating the optimal renewal period based on a yearly benefit cost ratio per pipe in a case study area, it was possible to draft an optimal renewal plan for the subject region from an economic perspective. Compared with other methodologies, a reasonable optimal renewal period was derived from an economic point of view. The result of this study may be used to develop future water pipe renewal plans. Moreover, the proposed methodologies and results derived from this study can be applied to asset management plans.

We developed a classification model and a real-time prediction model for short-term dissolved oxygen (DO) at the junction of the Han River in Anyangcheon, where water quality accidents occur frequently. The classification model is an analysis model that derives the main factors affecting DO changes in the Anyangcheon mobile water quality monitoring network using decision tree, random forest, and XGBoost. The model identified the key factors affecting DO changes to be electrical conductivity, cumulative precipitation, total nitrogen, and water temperature. Random forest (sensitivity, 0.9962; accuracy, 0.9981) and XGBoost (sensitivity, 1.0000; accuracy, 0.9822) showed excellent classification performance. The real-time prediction model for short-term DO we developed adopted artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms. LSTM (R2 = 0.93 − 0.97, first half; R2 = 0.95 − 0.96, second half) and GRU (R2 = 0.94 − 0.98, first half; R2 = 0.96 − 0.98, second half) significantly outperformed ANN (R2 = 0.64 − 0.86). The LSTM and GRU models we developed used real-time automatic measurement data, targeting urban rivers that are sensitive to water quality changes and are waterfront areas for citizens. They can quickly reflect and simulate short-term, real-time changes in water quality compared with existing static models.

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