A Pareto-optimal moving average-multigene genetic programming model for rainfall-runoff modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{DanandehMehr:2017:EMS,

• author = "Ali {Danandeh Mehr} and Vahid Nourani",
• title = "A {Pareto}-optimal moving average-multigene genetic programming model for rainfall-runoff modelling",
• journal = "Environmental Modelling \& Software",
• year = "2017",
• volume = "92",
• pages = "239--251",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Multigene genetic programming, Rainfall-runoff modelling, Pareto-optimal model, Multilayer perceptron, Moving average filtering",
• ISSN = "1364-8152",
• DOI = "doi:10.1016/j.envsoft.2017.03.004",
• URL = "http://www.sciencedirect.com/science/article/pii/S1364815216308143",
• size = "13 pages",
• 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 25-60percent lower and timing accuracy 80-760percent higher. Moreover, it is explicit and parsimonious, motivating to be used in practice.",

}

Genetic Programming entries for Ali Danandeh Mehr Vahid Nourani

Citations