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Recent catastrophic events such as Hurricane Katrina have highlighted the importance of interdependency to result in cascading effect among critical infrastructures. Previous studies investigated and categorized the interdependency of critical infrastructures based on qualitative consideration and case studies. However, it is still necessary to approach this quantitatively. The main purpose of this study is to model the behavior of interdependent infrastructures in urban systems, especially water and energy supply, and suggest management policy for water supply systems to increase its resilience. The relationship between two infrastructures was converted into causal loop diagrams. The system dynamics model considering the interdependency was developed with components and relationship from previous research. The capacity of water supply system and its recovery capacity under disruptive scenarios was assessed as the resilience of the water supply system. The simulation result showed that the order of interdependency has high sensitivity with the variance of resilience. It was also found that enhancing the resilience of the water supply system improved not only the recovery capacity of the water supply system but also of the energy supply system. The most efficient policy leverage was found in the enhancing feedback loop of resource exchange between two infrastructures. Some assumptions in the relationship between components should be formulated with more concrete observation and mathematical consideration in further studies.

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Particulate matter has become one of the major issues in environmental sustainability, and its accurate measurement has grown in importance recently. Low-cost sensors (LCS) have been widely used to measure particulate concentration, but concerns about their accuracy remain. Previous research has shown that LCS data can be successfully calibrated using various machine learning algorithms. In this study, for better calibration, dynamic weight was introduced to the loss function of the LSTM model to amplify the loss, especially in a specific band. Our results showed that the dynamically weighted loss function resulted in better calibration in the specific band, where the model accepts the loss more sensitively than outside of the band. It was also confirmed that the dynamically weighted loss function can improve the calibration of the LSTM model in terms of both overall performance and local performance in bands. In a test case, the overall calibration performance was improved by about 12.57%, from 3.50 to 3.06, in terms of RMSE. The local calibration performance in the band improved from 4.25 to 3.77. Such improvements were achieved by varying coefficients of the dynamic weight. The results from different bands also indicated that having more data in a band will guarantee better improvement.

Recently, the research on critical infrastructures have been extended its boundary and some previous research analysed interdependency between infrastructures tried to suggest operation and management policy of the infrastructure. This highlighted the importance of systemic approach such as System Dynamics. But there still exist the limit to select the modelling component in terms of causality. In this study, convergent cross mapping was applied to time series variables in Songpa-gu and Gangdong-gu in Seoul, South Korea. Selected variables include operating variable of water supply system, floating population and meteorological variables and 330 observations were collected. The result shows that daily average temperature and floating population that are equivalent to mobile population influences to the daily water supply, and vice versa. Other weather factors such as average precipitation and wind speed showed little causality with the daily water supply. The influential factors of water supply system can be investigated with convergent cross mapping and the result can be utilized for the pre-process of other methodology such as LSTM in short-term water demand prediction. It is expected to collect more available data to improve this study further.