Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control

Alternative Title

Using Trend Extraction and Machine Learning Methods to Improve Flood Modeling and Control

Abstract

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.

Department(s)

Engineering Management and Systems Engineering

Research Center/Lab(s)

INSPIRE - University Transportation Center

Second Research Center/Lab

Intelligent Systems Center

Keywords and Phrases

Trend extraction; Spatial and temporal trends; Images

International Standard Book Number (ISBN)

978-1-83962-944-0

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 IntechOpen, All rights reserved.

Publication Date

15 Feb 2021

Link to Full Text

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