Mapping Coastal Flood Susceptible Areas Using Shannon’s Entropy Model: The Case of Muscat Governorate, Oman

• Main Authors: Hanan Al-Hinai, Rifaat Abdalla
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: ISPRS International Journal of Geo-Information
• Subjects: ArcGIS software; coastal flood susceptibility index; digital elevation model DEM; influencing factors; precondition factors; trigger factors
• Online Access: https://www.mdpi.com/2220-9964/10/4/252

Floods are among the most common natural hazards around the world. Mapping and evaluating potential flood hazards are essential for flood risk management and mitigation strategies, particularly in coastal areas. Several factors play significant roles in flooding and recognizing the role of these flood-related factors may enhance flood disaster prediction and mitigation strategies. This study focuses on using Shannon’s entropy model to predict the role of seven factors in causing floods in the Governorate of Muscat, Sultanate of Oman, and mapping coastal flood-prone areas. The seven selected factors (including ground elevation, slope degree, hydrologic soil group (HSG), land use, distance from the coast, distance from the wadi, and distance from the road) were initially prepared and categorized into classes based on their contribution to flood occurrence. In the next step, the entropy model was used to determine the weight and contribution of each factor in overall susceptibility. Finally, results from the previous two steps were combined using ArcGIS software to produce the final coastal flood susceptibility index map that was categorized into five susceptibility zones. The result indicated that land use and HSG are the most causative factors of flooding in the area, and about 133.5 km² of the extracted area is threatened by coastal floods. The outcomes of this study can provide decision-makers with essential information for identifying flood risks and enhancing adaptation and mitigation strategies. For future work, it is recommended to evaluate the reliability of the obtained result by comparing it with a real flooding event, such as flooding during cyclones Gonu and Phet.