A framework for real-time operation of urban detention reservoirs: Application of the cellular automata and rainfall nowcasting.

Autor: Talebi A; School of Civil Engineering, College of Engineering, University of Tehran., Tehran, Iran., Dolatshahi M; School of Civil Engineering, College of Engineering, University of Tehran., Tehran, Iran., Kerachian R; School of Civil Engineering, College of Engineering, University of Tehran., Tehran, Iran. Electronic address: kerachian@ut.ac.ir.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2024 Jan 15; Vol. 350, pp. 119638. Date of Electronic Publication: 2023 Nov 28.
DOI: 10.1016/j.jenvman.2023.119638
Abstrakt: Detention reservoirs are employed in urban drainage systems to reduce peak flows downstream of reservoirs. In addition to the volume of detention reservoirs, their operational policies could significantly affect their performance. This paper presents a framework for the real-time coordinated operation of detention reservoirs using deep-learning-based rainfall nowcasting data. Considering the short concentration time of urban basins, the real-time operating policies of urban detention reservoirs should be developed quickly. In the proposed framework, a cellular automata (CA)-based optimization algorithm is linked with the storm water management model (SWMM) to optimize real-time operating policies of gates at the inlets and outlets of detention reservoirs. As CA-based optimization models are not population-based, their computational costs are much less than population-based metaheuristic optimization techniques such as genetic algorithms. To evaluate the applicability and efficiency of the framework, it is applied to the east drainage catchment (EDC) of Tehran metropolitan area in Iran. The results illustrate that the proposed framework could reduce the overflow volume by up to 60%. For complete flood control in the study area, in addition to the real-time operation of detention reservoirs, constructing five tunnels with a total length of 13200 m is recommended. To evaluate the performance of the CA-based optimization model, its results are compared with those obtained from the non-dominated sorting genetic algorithm III (NSGA-III). It is shown that the CA-based model provides similar results with only 5% of the run-time of NSGA-III. A sensitivity analysis is also performed to evaluate the effects of optimization models' parameters on their performance.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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