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Determining Flood Prone Areas for an...

Aburizaiza, Manal Omar.

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Determining Flood Prone Areas for an Ungauged Basin Using Soft Data and Topographic Wetness Index.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Determining Flood Prone Areas for an Ungauged Basin Using Soft Data and Topographic Wetness Index./
作者:	Aburizaiza, Manal Omar.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	148 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
標題:	Geographic information science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13880524
ISBN:	9781392236215

Determining Flood Prone Areas for an Ungauged Basin Using Soft Data and Topographic Wetness Index.
Aburizaiza, Manal Omar.

Determining Flood Prone Areas for an Ungauged Basin Using Soft Data and Topographic Wetness Index. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 148 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--George Mason University, 2019.

This item must not be added to any third party search indexes.

The rapid urban development in the basins of the city of Jeddah, Saudi Arabia, makes the residential area in the watershed vulnerable to flood hazards. Traditional flood models rely on data calibration to provide useful runoff estimation, but such data is not available in the ungagged basins of Jeddah. Therefore, this research aims to examine the feasibility of integrating soft data and topographic data in order to determine the flood-prone areas for an ungauged basin in Jeddah. It proposes a method to extract qualitative flood information such as spatial extent and flood depth from soft data obtained from social media platforms (Twitter and YouTube) and field visits. To obtain flood information from social media, the analysis considers three kinds of social media posts: text, visual, and audio. The information obtained is validated during field visits. Next, the utility of the extracted information is examined by analyzing the spatial distribution of the soft data on the generated Topographic Wetness Index (TWI) maps. The TWI method is a distributed method that is used to estimate the potential for water accumulation in a certain area in the catchment by estimating the hydrological slope and the upslope of the contributing catchment area. The risk area is determined based on the locations of high urban density in the watershed that are exposed to the locations with high potential for water accumulation. Finally, a flood hazard map is generated based on the risk area and the spatial distribution of the soft data. Based on the map, social media gives better results for a high-density urbanized area, while field visits give additional spatial information for lower-density and non-urbanized areas. Soft data is an important data resource in gaining flood information and determining flood-prone areas for an ungauged basin.

ISBN: 9781392236215Subjects--Topical Terms:

3432445
Geographic information science.

Determining Flood Prone Areas for an Ungauged Basin Using Soft Data and Topographic Wetness Index.
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500 $a Publisher info.: Dissertation/Thesis.
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520 $a The rapid urban development in the basins of the city of Jeddah, Saudi Arabia, makes the residential area in the watershed vulnerable to flood hazards. Traditional flood models rely on data calibration to provide useful runoff estimation, but such data is not available in the ungagged basins of Jeddah. Therefore, this research aims to examine the feasibility of integrating soft data and topographic data in order to determine the flood-prone areas for an ungauged basin in Jeddah. It proposes a method to extract qualitative flood information such as spatial extent and flood depth from soft data obtained from social media platforms (Twitter and YouTube) and field visits. To obtain flood information from social media, the analysis considers three kinds of social media posts: text, visual, and audio. The information obtained is validated during field visits. Next, the utility of the extracted information is examined by analyzing the spatial distribution of the soft data on the generated Topographic Wetness Index (TWI) maps. The TWI method is a distributed method that is used to estimate the potential for water accumulation in a certain area in the catchment by estimating the hydrological slope and the upslope of the contributing catchment area. The risk area is determined based on the locations of high urban density in the watershed that are exposed to the locations with high potential for water accumulation. Finally, a flood hazard map is generated based on the risk area and the spatial distribution of the soft data. Based on the map, social media gives better results for a high-density urbanized area, while field visits give additional spatial information for lower-density and non-urbanized areas. Soft data is an important data resource in gaining flood information and determining flood-prone areas for an ungauged basin.
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