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The increase in frequency and intensity of flooding has become a global challenge. Increased population, rapid urbanization, and climate change all aggravate flood frequency and losses. Flooding events often lead to ripple effects which are a series of interconnected events that are triggered by flood hazards and aggravated by their recurrence. Ripple effects are often hard to predict and assess due to the high uncertainties inherent in their nature and causes. Hence, this paper aims to model the ripple effects of floods using data mining algorithms. This research mainly focuses on transportation infrastructure rather than other critical systems. First, data were collected for multiple flood events in the states of New York and New Jersey and their associated ripple events. Second, the data was cleansed and preprocessed. Third, association rule analysis was conducted to identify the critical dependencies or key combinations between the occurrence of flooding events and the associated ripple effects. The results illustrate that the following events are the most critical ripple effects resulting from flooding events: obstruction on the roadway, accidents, and single-line traffic alternating directions. The result of this study can provide helpful information for decision-makers to model infrastructure dependence and interdependence, which is an important consideration in the development of resilience-based performance standards to reduce flood-related losses.
Mohammadi et al. (Mon,) studied this question.