Metaheuristics in Flood Disaster Management and Risk Assessment

TL;DR

This paper explores the application of metaheuristic algorithms like genetic algorithms and simulated annealing to optimize flood risk assessment and disaster management strategies across different communities.

Contribution

It introduces a novel risk assessment framework combined with metaheuristic optimization to improve flood disaster management decisions.

Findings

Simulated annealing produced more realistic flood management designs.

Genetic algorithms yielded extreme solutions, indicating different optimization behaviors.

The approach effectively balances multiple risk factors in flood risk assessment.

Abstract

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.