TITLE:
Flood Forecasting GIS Water-Flow Visualization Enhancement (WaVE): A Case Study
AUTHORS:
Timothy R. Petty, Nawajish Noman, Deng Ding, John B. Gongwer
KEYWORDS:
Geovisualization, Riverine Flooding, Geoanalytics Forecasting, Machine Learning, Emergency Management Decision Support
JOURNAL NAME:
Journal of Geographic Information System,
Vol.8 No.6,
December
22,
2016
ABSTRACT: Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level; 2) integrate data and model analysis results from multiple sources; 3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams; and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.