Date of Award
Spring 2025
Document Type
Thesis
Degree Name
Master of Science (MS) in Geographic Information Science & Technology
Department
Chemistry & Geosciences
Committee Chair
Dr. Saeideh Gharehchahi
Abstract
Flooding is a global challenge, with effects mostly experienced in developing countries due to insufficient data for effective flood risk assessment and management. Refugee settlements are particularly vulnerable to flooding due to their remote locations, high population density, and temporary shelters, necessitating flood susceptibility mapping to effectively mitigate risks and minimize damage prior to flooding events. This study assessed flood susceptibility in the Nyarugusu refugee camp, Tanzania, through Multi-criteria Decision Analysis (MCDA) and Analytical Hierarchy Process using remote sensing and Geographic Information Systems (GIS) approaches. The flood susceptibility map that resulted from this process categorizes flood-prone areas into three classes: high, moderate and low. The flood susceptibility map was then compared against a single event simulated flood map using a binary confusion matrix revealed moderate agreement, with an overall accuracy of 62.77%. The AHP model achieved a precision of 28.03%, a recall of 69.58%, and an F1 score of 40.01%, indicating that while it effectively captured many flood-prone areas, it also tended to overpredict. Analysis on flood exposure showed spatial variability in flood risk across the camp’s 14 zones, with Zone 9 having the highest population at risk. This research demonstrates that MCDA and AHP-based modeling can be considered for flood risk mapping in data-scarce refugee settings. The methodology is replicable in similar data scarce regions, although it requires further improvements and contextual adjustments offering a valuable tool for disaster preparedness, resilience planning, and informed humanitarian interventions.
Included in
Geographic Information Sciences Commons, Remote Sensing Commons, Spatial Science Commons