Flood Mitigation Data Analytics and Decision Support Framework: Iowa Middle Cedar Watershed Case Study

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Yazeed Alabbad, Enes Yildirim, Ibrahim Demir


Flooding is one of the most frequent natural disasters, causing billions of dollars in damage and threatening vulnerable communities worldwide. Although the impact of flooding can never be diminished, minimizing future losses is possible by taking structural or non-structural mitigation actions. Mitigation applications are often costly practices. However, they can be more feasible for long-term planning and protection. On the other hand, selecting a feasible option requires a comprehensive analysis of potential risk and damages and comparing the costs and benefits of different mitigation types. This paper presents a web-based decision support framework called Mitigation and Damage Assessment System (MiDAS) that analyzes flood risk and mitigation strategies at the community and property-level scopes. The system utilizes regulatory flood inundation maps, damage functions, property information, scenario-based climate projections, and mitigation inputs and guidelines from the Federal Emergency Management Agency (FEMA) and the United States Army Corps of Engineers (USACE). We analyzed the community-level analysis of three major cities in Eastern Iowa: Cedar Falls, Cedar Rapids, and Waterloo. The framework helps users select the appropriate flood mitigation measures based on various characteristics (e.g., foundation type, occupancy, square footage) and provides cost estimates for implementing measures. The system also provides a decision tree algorithm for analyzing and representing the mitigation decision by reviewing existing guidelines (e.g., FEMA, USACE). Implementation of mitigation measures can reduce the property's vulnerability and improve the response to a flooding event.




Civil and Environmental Engineering, Engineering, Environmental Studies, Geographic Information Sciences, Risk Analysis


decision support, flood mitigation, Flood Risk, Floodproofing, data analytics


Published: 2021-10-23 08:55

Last Updated: 2021-10-23 15:55


CC BY Attribution 4.0 International

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