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Beluga Whale Optimization Algorithm for Estimating Nonlinear Muskingum Model in Flood Routing
Using hydraulic and hydrological routing approaches, the transient flow is examined. The Muskingum model (MM), a hydrological routing methodology, is a reasonably accurate and useful technique for the calculation of the output hydrograph from a river reach. The storage and continuity equations serve as the foundation for the comparatively straightforward process of flood routing using the Muskingum method. The type and manner of solving determine how accurate the Muskingum method is. In order to solve the routing problem utilizing the Muskingum method, a new meta-heuristic algorithm known as the Beluga whale optimization algorithm (BWO) is used in this study. By taking into account the nonlinear lateral flow, the MM type in this article is the nonlinear 6th-type Muskingum model. In this work, four case studies are looked into. It is demonstrated that the BWO algorithm performs well in approximating the relevant MM parameters. For the first through fourth case studies in this study, the goal values that are determined by minimizing the sum of square errors are acquired to be 2.1, 50.6, 11.82, and 61.4 (m3/s)2, respectively.
Beluga Whale Optimization Algorithm for Estimating Nonlinear Muskingum Model in Flood Routing
Using hydraulic and hydrological routing approaches, the transient flow is examined. The Muskingum model (MM), a hydrological routing methodology, is a reasonably accurate and useful technique for the calculation of the output hydrograph from a river reach. The storage and continuity equations serve as the foundation for the comparatively straightforward process of flood routing using the Muskingum method. The type and manner of solving determine how accurate the Muskingum method is. In order to solve the routing problem utilizing the Muskingum method, a new meta-heuristic algorithm known as the Beluga whale optimization algorithm (BWO) is used in this study. By taking into account the nonlinear lateral flow, the MM type in this article is the nonlinear 6th-type Muskingum model. In this work, four case studies are looked into. It is demonstrated that the BWO algorithm performs well in approximating the relevant MM parameters. For the first through fourth case studies in this study, the goal values that are determined by minimizing the sum of square errors are acquired to be 2.1, 50.6, 11.82, and 61.4 (m3/s)2, respectively.
Beluga Whale Optimization Algorithm for Estimating Nonlinear Muskingum Model in Flood Routing
Iran J Sci Technol Trans Civ Eng
Moradi, Ehsan (author) / Yaghoubi, Behrouz (author) / Shabanlou, Saeid (author)
2024-06-01
17 pages
Article (Journal)
Electronic Resource
English
Beluga Whale Optimization Algorithm for Estimating Nonlinear Muskingum Model in Flood Routing
| Springer Verlag | 2024
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