Metaheuristics in Flood Disaster Management and Risk Assessment
This work addresses flood disaster management for specific communities, but it is incremental as it applies existing metaheuristics to a known risk assessment framework.
The study tackled flood risk assessment by dividing an area into barangays and evaluating vulnerability using weighted factors like urbanization and literacy, then compared optimization methods; results indicated simulated annealing produced extreme solutions while genetic algorithms yielded realistic designs.
A conceptual area is divided into units or barangays, each was allowed to evolve under a physical constraint. A risk assessment method was then used to identify the flood risk in each community using the following risk factors: the area's urbanized area ratio, literacy rate, mortality rate, poverty incidence, radio/TV penetration, and state of structural and non-structural measures. Vulnerability is defined as a weighted-sum of these components. A penalty was imposed for reduced vulnerability. Optimization comparison was done with MatLab's Genetic Algorithms and Simulated Annealing; results showed 'extreme' solutions and realistic designs, for simulated annealing and genetic algorithm, respectively.