Developing Blue Spots Model for Tennessee Using GIS, and Advanced Data Analytics: Literature Review  

作　　者：Fasesin Kingsley Fasesin Kingsley(Department of Computing and the Department of Geosciences, East Tennessee State University, Johnson City, Tennessee, USA)

机构地区：[1]Department of Computing and the Department of Geosciences, East Tennessee State University, Johnson City, Tennessee, USA

出　　处：《Journal of Geoscience and Environment Protection》2023年第6期145-154,共10页地球科学和环境保护期刊（英文）

摘　　要：Climate change and global warming results in natural hazards, including flash floods. Flash floods can create blue spots;areas where transport networks (roads, tunnels, bridges, passageways) and other engineering structures within them are at flood risk. The economic and social impact of flooding revealed that the damage caused by flash floods leading to blue spots is very high in terms of dollar amount and direct impacts on people’s lives. The impact of flooding within blue spots is either infrastructural or social, affecting lives and properties. Currently, more than 16.1 million properties in the U.S are vulnerable to flooding, and this is projected to increase by 3.2% within the next 30 years. Some models have been developed for flood risks analysis and management including some hydrological models, algorithms and machine learning and geospatial models. The models and methods reviewed are based on location data collection, statistical analysis and computation, and visualization (mapping). This research aims to create blue spots model for the State of Tennessee using ArcGIS visual programming language (model) and data analytics pipeline.Climate change and global warming results in natural hazards, including flash floods. Flash floods can create blue spots;areas where transport networks (roads, tunnels, bridges, passageways) and other engineering structures within them are at flood risk. The economic and social impact of flooding revealed that the damage caused by flash floods leading to blue spots is very high in terms of dollar amount and direct impacts on people’s lives. The impact of flooding within blue spots is either infrastructural or social, affecting lives and properties. Currently, more than 16.1 million properties in the U.S are vulnerable to flooding, and this is projected to increase by 3.2% within the next 30 years. Some models have been developed for flood risks analysis and management including some hydrological models, algorithms and machine learning and geospatial models. The models and methods reviewed are based on location data collection, statistical analysis and computation, and visualization (mapping). This research aims to create blue spots model for the State of Tennessee using ArcGIS visual programming language (model) and data analytics pipeline.

关 键 词：Blue Spots Floods Risks and Management GIS Hydrological Models GEOSPATIAL Model Builder LiDAR Data Remote Sensing Data Analytics Pipe-line 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]