Optimization of Structural Flood Mitigation Strategies

TL;DR

This paper introduces a computational framework combining simulation and physics-based optimization to design effective flood mitigation strategies, significantly improving solution quality over traditional methods.

Contribution

It develops a new discretization and optimization approach for flood mitigation design, integrating physics-based procedures to enhance convergence and solution quality.

Findings

Achieved an average of 65% improvement in mitigation solution quality

Demonstrated effectiveness on hypothetical dam break scenarios

Showed that meta-heuristics alone are insufficient for high-quality solutions

Abstract

The dynamics of flooding are primarily influenced by the shape, height, and roughness (friction) of the underlying topography. For this reason, mechanisms to mitigate floods frequently employ structural measures that either modify topographic elevation, e.g., through the placement of levees and sandbags, or increase roughness, e.g., through revegetation projects. However, the configuration of these measures is typically decided in an ad hoc manner, limiting their overall effectiveness. The advent of high-performance surface water modeling software and improvements in black-box optimization suggest that a more principled design methodology may be possible. This paper proposes a new computational approach to the problem of designing structural mitigation strategies under physical and budgetary constraints. It presents the development of a problem discretization amenable to simulation-based, derivative-free optimization. However, meta-heuristics alone are found to be insufficient for obtaining quality solutions in a reasonable amount of time. As a result, this paper proposes novel numerical and physics-based procedures to improve convergence to a high-quality mitigation. The efficiency of the approach is demonstrated on hypothetical dam break scenarios of varying complexity under various mitigation budget constraints. In particular, experimental results show that, on average, the final proposed algorithm results in a 65% improvement in solution quality compared to a direct implementation.