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Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm.

F di Pierro1, S Djordjević, Z Kapelan

  • 1School of Engineering, Computer Science and Mathematics, University of Exeter, North Park Road, Exeter EX4 4QF, United Kingdom. F.di-Pierro@exeter.ac.uk

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|October 27, 2005
PubMed
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This study introduces a new Preference Ordering Genetic Algorithm (POGA) to improve urban drainage model calibration. POGA effectively handles multiple objectives, overcoming limitations of traditional Pareto-based methods for better sewer network modeling.

Area of Science:

  • Environmental Engineering
  • Computational Fluid Dynamics
  • Water Resource Management

Background:

  • Urban drainage model calibration requires balancing multiple, often conflicting, objectives.
  • Conventional Multi-Objective Genetic Algorithms (MOGAs) using Pareto efficiency face challenges with increasing criteria, leading to search inefficiency and decision-maker overload.
  • Existing MOGAs struggle to present a manageable set of optimal solutions when numerous criteria are involved.

Purpose of the Study:

  • To propose and evaluate a novel Multi-Objective Genetic Algorithm (MOGA) designed to improve the calibration of urban drainage models.
  • To address the limitations of Pareto-based MOGAs, particularly the issue of an overwhelming number of non-dominated solutions as the number of objectives increases.
  • To introduce a Preference Ordering Genetic Algorithm (POGA) that enhances the efficiency of the search process and aids decision-making in sewer network modeling.

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Main Methods:

  • Development of a new Multi-Objective Genetic Algorithm (MOGA) termed the Preference Ordering Genetic Algorithm (POGA).
  • Application of POGA to calibrate a physically-based, distributed sewer network model.
  • Comparative analysis of POGA's performance against NSGA-II, a widely recognized MOGA, using established calibration criteria.

Main Results:

  • The proposed Preference Ordering Genetic Algorithm (POGA) demonstrates efficacy in calibrating a complex urban drainage model.
  • POGA alleviates the drawbacks of conventional Pareto-based methods, particularly in scenarios with multiple calibration objectives.
  • Comparative results indicate that POGA offers an improved approach over NSGA-II for sewer network model calibration, managing the trade-offs between efficiency and solution presentation.

Conclusions:

  • The Preference Ordering Genetic Algorithm (POGA) presents a viable advancement over traditional Pareto-based Multi-Objective Genetic Algorithms for urban drainage model calibration.
  • POGA effectively addresses the challenge of managing multiple calibration criteria, enhancing both algorithmic search efficiency and the practical utility of the results for decision-makers.
  • This novel approach contributes to more robust and efficient sewer network modeling and management through improved calibration techniques.