A systematic approach to determining metamodel scope for risk-based optimization and its application to water distribution system design. (July 2015)

• Record Type: Journal Article
• Authors: Broad, Darren R.; Dandy, Graeme C.; Maier, Holger R.
• Is Part Of: Environmental modelling & software. Volume 69(2015:Jul.)
• Page Start: 382
• Page End: 395
• Publication Date: 2015-07
• Subjects: Optimization -- Uncertainty -- Risk -- Monte Carlo Simulation -- Artificial Neural Networks -- Metamodelling -- Water resources -- Water distribution systems; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.11.015
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Metamodels have proven be very useful when it comes to reducing the computational requirements of Evolutionary Algorithm-based optimization by acting as quick-solving surrogates for slow-solving fitness functions. The relationship between metamodel scope and objective function varies between applications, that is, in some cases the metamodel acts as a surrogate for the whole fitness function, whereas in other cases it replaces only a component of the fitness function. This paper presents a formalized qualitative process to evaluate a fitness function to determine the most suitable metamodel scope so as to increase the likelihood of calibrating a high-fidelity metamodel and hence obtain good optimization results in a reasonable amount of time. The process is applied to the risk-based optimization of water distribution systems; a very computationally-intensive problem for real-world systems. The process is validated with a simple case study (modified New York Tunnels) and the power of metamodelling is demonstrated on a real-world case study (Pacific City) with a computational speed-up of several orders of magnitude. Highlights: Generic scope definition process for maximizing metamodel performance. Significant speed improvements while maintaining risk-based optimization performance using metamodels. Real-world water distribution system optimization application enabled through metamodelling.