Application Of Neural Networks And Genetic Programming To Rainfall Runoff modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@TechReport{drecourt:1999uANNGPrrmTR,

• 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 = "http://projects.dhi.dk/d2k/Publications/D2K-TR-0699-01.pdf",
• 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 prediction.",
• notes = "Cited by \cite{Freire:2010:ICEC} See also \cite{drecourt:1999uANNGPrrm}",
• size = "38 pages",

}

Genetic Programming entries for Jean-Philippe Drecourt

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