Use of a Bayesian Network for storm-induced flood risk assessment and effectiveness of ecosystem-based risk reduction measures in coastal areas (Port of Sur, Sultanate of Oman)

Abstract

A Bayesian Network (BN) approach is utilized to leverage synthetic datasets to aid in forecasting onshore hazards and Disaster Risk Reduction (DRR) measures for Port of Sur is assessed. The 3rd generation MIKE21 model was used to simulate flood hazard characteristics (depth and velocity) and the effectiveness of the mangrove forests along the shoreline as an ecosystem-based DRR measure through a reduction in depth and velocity for a 400-year storm return period event. The implementation of the ecosystem-based DRR reduced high damage (more than 64% damage) for residential buildings by 10%. Meanwhile, in the west part of the port, 25% of the population is at extreme risk to life, which can be reduced to 4% by implementing the ecosystem-based DRR. The ecosystem-based DRR reduced the high level of damage (more than 65% damage) for infrastructures by 6% and 9% for the eastern and central parts of the Port of Sur, respectively. The outcomes demonstrate that BN can be used as a simulation tool to conceptualize the potential consequences of a storm in Decision Support System (DSS). For coastal managers to evaluate which options are the best for reducing potential damage to coastal systems.