Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis

Abstract

Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being able to accurately estimate the roughness coefficient, also known as Manning’s n coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ‘n’. First, an objective function is designed for measuring the quality of ‘candidate’ Manning’s coefficients relative to specif cross-sections of a river. Second, such function is optimised to return coefficients having the highest quality as possible. Five well-known meta-heuristic algorithms are employed to achieve this goal, these being a classic Evolution Strategy, a Differential Evolution algorithm, the popular Covariance Matrix Adaptation Evolution Strategy, a classic Particle Swarm Optimisation and a Bayesian Optimisation framework. We report results on two real-world case studies based on the Italian rivers ‘Paglia’ and ‘Aniene’. A comparative analysis between the employed optimisation algorithms is performed and discussed both empirically and statistically. From the hydrodynamic point of view, the experimental results are satisfactory and produced within significantly less computational time in comparison to classic methods. This shows the suitability of the proposed approach for optimal estimation of the roughness coefficient and, in turn, for designing optimised hydrological models.

Adaptive Salp Swarm Algorithm for Optimization of Geotechnical Structures

“…where j ≥ 2. In case some agents transfer outside of the search area, Equation (6) shows how to move salps back into the search area if they leave it:…”

“…As a result, traditional deterministic optimization techniques based on mathematical principles may struggle to find a global optimum solution due to local optima trapping. The use of powerful metaheuristic optimization algorithms for obtaining a global optimum to overcome this limitation is of interest, and metaheuristic algorithms have proven to be an excellent alternative for solving complex problems in recent decades [1][2][3][4][5][6].…”