A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction

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

  author =       "Ali {Danandeh Mehr} and Ercan Kahya",
  title =        "A Pareto-optimal moving average multigene genetic
                 programming model for daily streamflow prediction",
  journal =      "Journal of Hydrology",
  volume =       "549",
  pages =        "603--615",
  year =         "2017",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2017.04.045",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0022169417302664",
  abstract =     "Genetic programming (GP) is able to systematically
                 explore alternative model structures of different
                 accuracy and complexity from observed input and output
                 data. The effectiveness of GP in hydrological system
                 identification has been recognized in recent studies.
                 However, selecting a parsimonious (accurate and simple)
                 model from such alternatives still remains a question.
                 This paper proposes a Pareto-optimal moving average
                 multigene genetic programming (MA-MGGP) approach to
                 develop a parsimonious model for single-station
                 streamflow prediction. The three main components of the
                 approach that take us from observed data to a validated
                 model are: (1) data pre-processing, (2) system
                 identification and (3) system simplification. The data
                 pre-processing ingredient uses a simple moving average
                 filter to diminish the lagged prediction effect of
                 stand-alone data-driven models. The multigene
                 ingredient of the model tends to identify the
                 underlying nonlinear system with expressions simpler
                 than classical monolithic GP and, eventually
                 simplification component exploits Pareto front plot to
                 select a parsimonious model through an interactive
                 complexity-efficiency trade-off. The approach was
                 tested using the daily streamflow records from a
                 station on Senoz Stream, Turkey. Comparing to the
                 efficiency results of stand-alone GP, MGGP, and
                 conventional multi linear regression prediction models
                 as benchmarks, the proposed Pareto-optimal MA-MGGP
                 model put forward a parsimonious solution, which has a
                 noteworthy importance of being applied in practice. In
                 addition, the approach allows the user to enter human
                 insight into the problem to examine evolved models and
                 pick the best performing programs out for further
  keywords =     "genetic algorithms, genetic programming, Streamflow
                 prediction, Pareto-optimal, Hydrological modelling",

Genetic Programming entries for Ali Danandeh Mehr Ercan Kahya