Genetic Programming for Modelling Long-Term Hydrological Time Series

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Wang:2009:ICNC,

• author = "Wenchuan Wang and Dongmei Xu and Lin Qiu and Jianqin Ma",
• title = "Genetic Programming for Modelling Long-Term Hydrological Time Series",
• booktitle = "Fifth International Conference on Natural Computation, ICNC '09",
• year = "2009",
• month = aug,
• volume = "4",
• pages = "265--269",
• keywords = "genetic algorithms, genetic programming, artificial neural network, autocorrelation function, evolutionary computing method, flow prediction method, hydrological time series forecasting, lagged input variable, monthly river flow discharge, reservoir inflow sequence data, root mean square error, transparent-structured system identification, channel flow, correlation methods, forecasting theory, identification, mean square error methods, neural nets, prediction theory, time series",
• DOI = "doi:10.1109/ICNC.2009.210",
• abstract = "In recent years, artificial neural networks (ANN) have emerged as a novel identification technique for the forecasting of hydrological time series. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. The purpose of this study is to develop a flow prediction method, based on the genetic programming (GP), which is an evolutionary computing method that provides `transparent' and structured system identification. In terms of statistical characteristic of reservoir inflow sequence data, the autocorrelation function is employed to make certain amount of lagged input variables and the root mean square error is adopted as fitness of evaluation. The GP model is examined using the long-term observations of monthly river flow discharges. Through the comparison of its performance with those of the ANN, it is demonstrated that the GP model is an effective algorithm to forecast the long-term discharges.",
• notes = "Also known as \cite{5366249}",

}

Genetic Programming entries for Wen-Chuan Wang Dongmei Xu Lin Qiu Jianqin Ma

Citations