Optimizing the urban runoff collecting network using Harris Hawks single-objective optimization algorithm (a case study of the flood network in West Tehran)

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Majidi Seyed Masoud; Zarif Sanayei Hamed Reza; Raeisi Isa Abadi Ali

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Journal Paper

Paper Information

Journal: JOURNAL OF HYDRAULIC STRUCTURES Year:2025 | Volume:11 | Issue:1 Page(s): 1-19

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Information Journal Paper

Title

Optimizing the urban runoff collecting network using Harris Hawks single-objective optimization algorithm (a case study of the flood network in West Tehran)

Author(s)

Majidi Seyed Masoud | Zarif Sanayei Hamed Reza | Raeisi Isa Abadi Ali | Issue Writer Certificate

Keywords

Harris Hawks Optimization

rainfall

Surface Water

Runoff Collecting Network

flood management

Abstract

Today, optimization is very important and necessary in engineering works and it seems necessary in the field of urban runoff and flood management. In this research, a case study of Darakeh River in Tehran as an important links in flood management and runoff conveying is used to reduce the costs of constructing flood conveying routs and water leakage. In the next step, rainfall values are obtained in return periods of 20, 25, 35, 50, 75 and 100 years by using hydrological relationships. Then, the output runoff is extracted by modeling the network in numerical software and the output hydrographs of the created model areentered into SWMM software. In order to present the output results of the SWMM model in a better format, the SWMM results are entered into SSA software. The SSA results are linked into the Harris Hawks Optimization (HHO) algorithm and finally, the construction cost and water leakage functions are optimized. Eventually, the results of HHO are compared with the Particle Swarm Optimization (PSO) algorithm to check the accuracy of HHO. The value of changes for width and height in bridges and channels are compared in two algorithms. As a result, the HHO algorithm reduces the volume of flood and water leakage in different return periods by presenting less cost as compared to the PSO algorithm.

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