Data Mining Dynamic Human Behaviors for Flood Risk Assessment in Coupled Human-environment Systems (2020 - )
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. They rely on statistical methods and hydrological models to quantify the expected risk as a function of hazard (i.e., flood extent depth), exposure (of people/assets), and vulnerability (damage). 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. One of the key challenges in quantitative risk assessment at present is how to integrate information on dynamic human behaviors in risk assessment models. These should then inform precautionary and emergency measures and risk management approaches which mitigate flood risk.
This project addresses the above challenge as articulated in these three specific questions: (i) which data and approaches can be utilized to better understand and model relevant human behaviors before, during, and after flood events? (ii) how can relevant human behaviors be learned from the above data and integrated in dynamic flood risk assessments to support decision-making in risk management and climate adaptation? (iii) which environmental and economic benefits can be achieved by embedding human behaviors in flood risk models?
Peer-reviewed Publications (journal or conference)
- N. Veigel, H. Kreibich, and A. Cominola (2022). A gradient boosting approach to identify behavioral and policy determinants of flood resilience in the continental US. IFAC-PapersOnLine, 55, 33, 85–91. https://doi.org/10.1016/j.ifacol.2022.11.014
Other (presentations at conferences or preprints)
- N. Veigel, H. Kreibich, and A. Cominola. Mining flood insurance big data to reveal the determinants of humans' flood resilience. (Oral presentation), EGU General Assembly, Online, 19–30 Apr 2021, EGU21-3042, https://doi.org/10.5194/egusphere-egu21-3042
- N. Veigel, H. Kreibich, and A. Cominola. Mining flood insurance big data to incorporate behavioural and social aspects in flood risk modelling (Poster presentation), AGU Fall Meeting, Online & New Orleans, USA, 13–17 Dec 2021, SY55D-0390
- N. Veigel, H. Kreibich, and A. Cominola. Exploring Behavioral Determinants of Flood Insurance Adoption with Explainable Machine Learning in the Continental US.EGU General Assembly, Vienna, Austria & Online, 23-27 May 2022. EGU22-5839, https://doi.org/10.5194/egusphere-egu22-5839