A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Wang2009294,
-
author = "Wen-Chuan Wang and Kwok-Wing Chau and
Chun-Tian Cheng and Lin Qiu",
-
title = "A comparison of performance of several artificial
intelligence methods for forecasting monthly discharge
time series",
-
journal = "Journal of Hydrology",
-
volume = "374",
-
number = "3-4",
-
pages = "294--306",
-
year = "2009",
-
ISSN = "0022-1694",
-
DOI = "doi:10.1016/j.jhydrol.2009.06.019",
-
URL = "http://www.sciencedirect.com/science/article/B6V6C-4WK48G6-1/2/7cf0d9cf0adb10d24201878b9773ca27",
-
keywords = "genetic algorithms, genetic programming, Monthly
discharge time series forecasting, ARMA, ANN, ANFIS,
GP, SVM",
-
abstract = "Developing a hydrological forecasting model based on
past records is crucial to effective hydropower
reservoir management and scheduling. Traditionally,
time series analysis and modeling is used for building
mathematical models to generate hydrologic records in
hydrology and water resources. Artificial intelligence
(AI), as a branch of computer science, is capable of
analyzing long-series and large-scale hydrological
data. In recent years, it is one of front issues to
apply AI technology to the hydrological forecasting
modeling. In this paper, autoregressive moving-average
(ARMA) models, artificial neural networks (ANNs)
approaches, adaptive neural-based fuzzy inference
system (ANFIS) techniques, genetic programming (GP)
models and support vector machine (SVM) method are
examined using the long-term observations of monthly
river flow discharges. The four quantitative standard
statistical performance evaluation measures, the
coefficient of correlation (R), Nash-Sutcliffe
efficiency coefficient (E), root mean squared error
(RMSE), mean absolute percentage error (MAPE), are
employed to evaluate the performances of various models
developed. Two case study river sites are also provided
to illustrate their respective performances. The
results indicate that the best performance can be
obtained by ANFIS, GP and SVM, in terms of different
evaluation criteria during the training and validation
phases.",
- }
Genetic Programming entries for
Wen-Chuan Wang
Kwok-Wing Chau
Chun-Tian Cheng
Lin Qiu
Citations