Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabil...

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Published in:Infrastructures (Basel) Vol. 4; no. 2; p. 35
Main Authors: Ngamalieu-Nengoue, Ulrich A., Iglesias-Rey, Pedro L., Martínez-Solano, F. Javier
Format: Journal Article
Language:English
Published: Basel MDPI AG 08.06.2019
Subjects:
Adaptation
Climate change
Costs
Drainage
drainage networks
extreme rainfalls
Flood damage
Floods
Genetic algorithms
Methodology
Methods
multi-objective optimization
Multiple objective analysis
Pareto optimization
problem size reduction
Public spaces
Reduction
Rehabilitation
Runoff
Searching
Sedimentation & deposition
Sorting algorithms
Storage tanks
Storm damage
Stormwater management
SWMM
ISSN:2412-3811, 2412-3811
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Abstract The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multi-objective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models’ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions.
AbstractList The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multi-objective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models’ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions.
Author Ngamalieu-Nengoue, Ulrich A.
Iglesias-Rey, Pedro L.
Martínez-Solano, F. Javier
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  surname: Martínez-Solano
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StartPage 35
SubjectTerms Adaptation
Climate change
Costs
Drainage
drainage networks
extreme rainfalls
Flood damage
Floods
Genetic algorithms
Methodology
Methods
multi-objective optimization
Multiple objective analysis
Pareto optimization
problem size reduction
Public spaces
Reduction
Rehabilitation
Runoff
Searching
Sedimentation & deposition
Sorting algorithms
Storage tanks
Storm damage
Stormwater management
SWMM
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Title Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology
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