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

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  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