Data Science and Digital Business

  • Lei Bu
  • Feng WangEmail author


This book chapter applies data science methods to analyze storm surge induced flood risks along the Mississippi Gulf Coast by presenting the spatial risk distribution of the study area using the Geographic Information System (GIS) based visualization and quantifying the flood risk in statistical relationships with the risk related factors using multiple linear regression analysis models. The data are retrieved and visualized for the residential blocks. The maximum surge elevation data are collected and validated against representative historical hurricane wind and storm surge data recorded by the Federal Emergency Management Agency (FEMA) and National Hurricane Center (NHC). The maximum surge height above the land surface is calculated based on the elevations and tide level in the Mississippi Gulf Coast Basin. The statistics models using the multiple regression analysis method characterize the significant relationships among these risk related variables. The direct loss coverage can be estimated using the models.

List of Abbreviations


Geographic Information System


North American Vertical Datum


Sea, Lake and Overland Surges from Hurricanes


National Oceanic and Atmospheric Administration


Federal Emergency Management Agency


National Hurricane Center


Hurricane Protection System


Average Daily Traffic


Digital Elevation Model


Mississippi Department of Transportation


Condition Index


Variance Inflator Factor


University Transportation Center



The project was partially funded by the Institute for Multimodal Transportation (IMTrans) at Jackson State University through the UTC program of the US Department of Transportation (USDOT).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringInstitute for Multimodal Transportation, Jackson State UniversityJacksonUSA
  2. 2.Ingram School of EngineeringTexas State UniversitySan MarcosUSA

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