Flood mitigation data analytics and decision support framework: Iowa Middle Cedar Watershed case study

Yazeed Alabbad; Enes Yildirim; Ibrahim Demir

doi:10.1016/j.scitotenv.2021.152768

Flood mitigation data analytics and decision support framework: Iowa Middle Cedar Watershed case study

Sci Total Environ. 2022 Mar 25:814:152768. doi: 10.1016/j.scitotenv.2021.152768. Epub 2022 Jan 4.

Authors

Yazeed Alabbad¹, Enes Yildirim², Ibrahim Demir³

Affiliations

¹ Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA, USA; Department of Civil Engineering, King Saud University, Riyadh, Saudi Arabia. Electronic address: yazeed-alabbad@uiowa.edu.
² Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA, USA.
³ Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA, USA; IIHR - Hydroscience and Engineering, University of Iowa, Iowa City, IA, USA.

PMID: 34990656
DOI: 10.1016/j.scitotenv.2021.152768

Abstract

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 impacts and mitigation strategies at the community and property-level with the goal of informing communities on the consequences of flooding and mitigation alternatives and encouraging them to participate in the community rating system. The framework 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). It will help users select the appropriate flood mitigation measures based on various characteristics (e.g., foundation type, occupancy, square footage) and provide 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). We analyzed the community-level mitigation for three major cities in Eastern Iowa (Cedar Falls, Cedar Rapids, and Waterloo) and found certain measures (e.g., wet/dry floodproofing) are cost-effective for community-level mitigation. Implementation of mitigation measures can reduce the property's vulnerability and improve the response to a flooding event.

Keywords: Data analytics; Decision support; Flood mitigation; Flood risk; Floodproofing.

MeSH terms

Cities
Data Science
Disasters*
Floods*
Iowa