Data Mining Human Behaviors for Flood Risk Assessment

Flooding events in urban and non-urban settings represent a major cause of insured losses, with costs of several billion US$/year globally (e.g., US$ 60 billion in 2016). Both people and infrastructural assets are vulnerable to flood events, which are becoming increasingly frequent and severe as a consequence of climate change, extreme rainfall events, and the increasing number of people living in flood-prone areas.

Several quantitative approaches for flood risk assessment exist in the literature. Yet, there is limited integration of dynamic human behaviors in such methods. Human behavior dynamics play a key role in affecting the impact and recovery time of floods in coupled human-environment systems. The perception of risk, as well as adaptive behaviors for prevention, preparation, response, and recovery during flood events (e.g., accessing weather warnings, donating money for recovery) depend on several individual and collective socio-psychographic determinants.

In this project we want to integrate novel data sources and data-driven methods to identify patterns in human responses to flooding to inform disaster risk management approaches and improve risk assessment models. We will be working with a variety of datasets including socio-economic, insurance, surveillance camera and social media data to capture various aspects of flood-related human behaviour during different stages of a disaster and across scales and resolutions.

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