Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control
Effective management of flood events depends on a thorough understanding of regional geospatial characteristics, yet data visualization is rarely effectively integrated into the planning tools used by decision makers. This chapter considers publicly available data sets and data visualization techniques that can be adapted for use by all community planners and decision makers. A long short-term memory (LSTM) network is created to develop a univariate time series value for river stage prediction that improves the temporal resolution and accuracy of forecasts. This prediction is then tied to a corresponding spatial flood inundation profile in a geographic information system (GIS) setting. The intersection of flood profile and affected road segments can be easily visualized and extracted. Traffic decision makers can use these findings to proactively deploy re-routing measures and warnings to motorists to decrease travel-miles and risks such as loss of property or life.
J. Hale et al., "Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control," Data Visualization, Feb 2021.
The definitive version is available at https://doi.org/10.5772/intechopen.96347
Engineering Management and Systems Engineering
INSPIRE - University Transportation Center
Keywords and Phrases
Trend extraction; Spatial and temporal trends; Images
International Standard Book Number (ISBN)
Article - Journal
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15 Feb 2021