Rapid Flood Damage Prediction and Forecasting Using Public Domain Cadastral and Address Point Data with Fuzzy Logic Algorithms
Abstract
National Flood Interoperability Experiment (NFIE) derived technologies and workflows will offer the ability to rapidly forecast flood damages. Address Points used by emergency management personnel approximate the locations of buildings, and they are a common operating picture for emergency responders. Most United States (U.S.) county tax assessment offices throughout the contiguous U.S. (CONUS) produce georeferenced cadastral data. To varying degrees, these parcel data describe building characteristics of structures within the parcel. Address Point data with cadastral data offers the ability to rapidly develop building inventories for flood damage estimation. Flood damage forecasts can expedite recovery and improve short‐term flood resilience. In this work the authors evaluate Flood Damage Wizard, a proposed open source platform independent methodology. Flood Damage Wizard uses point shapefile building information to estimate flood damage to buildings by finding the appropriate depth‐damage function using fuzzy‐text matching. The authors apply Flood Damage Wizard using Address Point and parcel datasets to demonstrate a method of estimating flood damage to buildings nearly anywhere within the CONUS. Results indicate using Address Point and cadastral datasets can generate total flood damage estimates approximate to those estimated using existing software solutions Hazus‐MH and HEC‐FIA with minimal manual processing of input data.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 08, 2017
- Source ID
- 10.1111/1752-1688.12556
Entities
People
- A.a. Oubeidillah
- A.n.s. Ernest
- Joseph Gutenson
- Lingling Zhu
- S.t. Sadeghi
- Xiaoxuan Zhang
Organizations
- Coastal and Hydraulics Laboratory
- United States Department of Defense
- University of Alabama
- University of Texas Rio Grande Valley