Assessment of Flash Flood Hazard in A Semiarid Area Through Satellite and Social Media Data Mining

Simple item page
dc.contributor.advisorSun, Dr. Donglian DS
dc.creatorALZahrani, Eidah
dc.date.accessioned2023-03-17T19:05:21Z
dc.date.available2023-03-17T19:05:21Z
dc.date.issued2022
dc.description.abstractSince 2009, flood events have shown an increasing trend in Saudi Arabia. Moreover, most floods occur in cities and may be related to rapid urbanization. Meanwhile, flooding in semiarid areas is usually short-term flash floods within small areas. Therefore, satellite-based flash flood observations are still challenging, while conventional observations are usually sparse in semiarid areas. This study combines machine learning techniques, the statistical analysis of frequency ratio, the logistic regression, and the analytic hierarchy process (AHP) method to identify flood-prone areas in a semiarid area in southern Saudi Arabia. This study integrates thirteen flood-contributing factors such as rainfall, elevation, aspect, slope, flow accumulation, stream power index (SPI), topographic wetness index (TWI), drainage density, distance from the river, distance from roads, soil types, urban area as represented by impervious area, and normalized difference vegetation index (NDVI). Ground observations from social media, such as Twitter and YouTube, validate the prediction results. The objectives of this study include: First, analyze the impacts of the selected thirteen flood-contributing factors. Second, build a decision-tree model between a flash flood and the influencing factors. Third, create a flood susceptibility map in southern Saudi Arabia using the AHP method. The susceptibility map shows the levels of flood risk and their respective percentages in the study area: very low 5%, low 44%, moderate 39%, high 1%, and very high 11%. The results are validated against the ground observations from social media, such as Twitter and YouTube. This research indicates 30.76% commission error and 35.71% omission error from the derived flood susceptibility map with very high and high flood risks, while the overall accuracy can reach 90.37%.
dc.format.extent114 pages
dc.format.mediumdoctoral dissertations
dc.identifier.urihttps://hdl.handle.net/1920/13092
dc.language.isoen
dc.rightsCopyright 2022 Eidah ALZahrani
dc.rights.urihttps://rightsstatements.org/vocab/InC/1.0
dc.subjectData Mining
dc.subjectFlash Flood
dc.subjectFlood
dc.subjectGIS
dc.subjectSatellite
dc.subjectSocial Media
dc.subject.keywordsGeographic information science and geodesy
dc.subject.keywordsGeography
dc.titleAssessment of Flash Flood Hazard in A Semiarid Area Through Satellite and Social Media Data Mining
dc.typeText
thesis.degree.disciplineGeoinformatics and Geospatial Intelligence
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral
thesis.degree.namePh.D. in Geoinformatics and Geospatial Intelligence

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ALZahrani_gmu_0883E_12783.pdf
Size:
3.21 MB
Format:
Adobe Portable Document Format
Download

Collections

College of Science