A Pareto-optimal moving average-multigene genetic programming model for rainfall-runoff modelling. (June 2017)

• Record Type: Journal Article
• Authors: Danandeh Mehr, Ali; Nourani, Vahid
• Is Part Of: Environmental modelling & software. Volume 92(2017)
• Page Start: 239
• Page End: 251
• Publication Date: 2017-06
• Subjects: Multigene genetic programming -- Rainfall-runoff modelling -- Pareto-optimal model -- Multilayer perceptron -- Moving average filtering; 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.2017.03.004
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The effectiveness of genetic programming (GP) in rainfall-runoff modelling has been recognized in recent studies. However, it may produce misleading estimations if autoregressive relationship between runoff and its antecedent values is not carefully considered. Meanwhile, GP evolves alternative models of different accuracy and complexity, where selecting a parsimonious model from such alternatives needs extra attention. To cope with these problems, this paper proposes a new hybrid model that integrates moving average filtering with multigene GP and uses Pareto-front plot to optimize the evolved models through an interactive complexity-efficiency trade-off. The model was applied to develop single- and multi-day-ahead rainfall-runoff models and compared to stand-alone GP, multigene GP, and multilayer perceptron as the benchmarks. The results indicated that the new model provides substantial improvements relative to the benchmarks, with prediction errors 25–60% lower and timing accuracy 80–760% higher. Moreover, it is explicit and parsimonious, motivating to be used in practice. Highlights: We developed a Pareto-optimal model integration moving average with multigene genetic programming for runoff prediction. The new model is explicit and less complex than stand-alone genetic programming models. Lagged prediction is the main drawback of stand-alone genetic programming models.