Application Of Neural Networks And Genetic Programming To Rainfall Runoff modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Jean-Philippe Drecourt",
  title =        "Application Of Neural Networks And Genetic Programming
                 To Rainfall Runoff modeling",
  institution =  "Danish Hydraulic Institute (Hydro-Informatics
                 Technologies HIT)",
  year =         "1999",
  type =         "D2K Technical Report",
  number =       "D2K-0699-1",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  broken =       "",
  abstract =     "The main problem in rainfall/runoff modeling is to
                 obtain data about the catchment with sufficient
                 accuracy. Since self-learning tools only need knowledge
                 about rainfall and runoff, they can offer a good
                 alternative to classical model. The present study
                 focuses on Lindenborg, a Danish catchment situated in
                 the northern part of Jutland, between Hobro and Alborg.
                 It is characterized by high groundwater contribution
                 and thus a very persistent flow regime. The tools used
                 were artificial neural networks (ANN) and genetic
                 programming (GP). The purpose was to compare the
                 efficiency of these tools with a classic lumped model
                 (NAM) and a naive prediction (i.e. the runoff does not
                 change between one day and the next one). The study
                 with GP was oriented in two directions: the prediction
                 of the runoff, and the prediction of the variation in
                 the runoff. In both cases GP was given the rainfall and
                 runoff of the past days, and it was assumed that the
                 rainfall was predicted without any error for the target
                 day. Each strategy has its own advantages. Predicting
                 the variation is considered to be closer to the
                 relationships given by physics, whereas predicting the
                 runoff takes in account the large auto-correlation of
                 the runoff time series. Since it is difficult to
                 predict the upper boundary of runoff, the ANN worked
                 exclusively with the time variation. The variation in
                 runoff is less likely to saturate the network than the
                 runoff itself, especially in this catchment where the
                 dynamics are relatively slow. Therefore, the
                 sensitivity of the prediction is increased. Time lag
                 recurrent network (TLRN) were used for this study as
                 they allow to take in account smoothed version of the
                 past time series, both in the input and the hidden
                 layers. The comparison of the different models was
                 based on the Pearson coefficient of correlation, which
                 gives a good overview of the performance of the
  notes =        "Cited by \cite{Freire:2010:ICEC} See also
  size =         "38 pages",

Genetic Programming entries for Jean-Philippe Drecourt