Short-term streamflow forecasting: ARIMA vs neural networks

被引：0
|
作者：
Frausto-Solis, Juan [1 ]
Pita, Esmeralda [2 ]
Lagunas, Javier [2 ]
机构：
[1] Tecnol Monterrey, Campus Cuernavaca Autopista Sol Km 104,Colonia Re, Xochitepec 62790, Morelos, Mexico
[2] Inst Investigaciones Elect, Cuernavaca 62490, Morelos, Mexico
来源：
RECENT ADVANCES ON APPLIED MATHEMATICS: PROCEEDINGS OF THE AMERICAN CONFERENCE ON APPLIED MATHEMATICS (MATH '08) | 2008年
关键词：
auto regressive integrated moving average; artificial neural networks; streamflow; forecasting;
D O I：
暂无
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of a Mexican river. Surprising results showed that in a monthly basis, ARIMA has lower prediction errors than this Neural Network.
引用
收藏
页码：402 / +
页数：2
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