Comparison of Wavelet Based Hybrid Models for Daily Evapotranspiration Estimation using Meteorological Data

被引:13
|
作者
Partal, Turgay [1 ]
机构
[1] Ondokuz Mayis Univ, Fac Engn, Dept Civil Engn, Samsun, Turkey
关键词
wavelet transformation; radial basis neural network; feed forward neural network; multi linear regression; evapotranspiration; estimating; SUSPENDED SEDIMENT DATA; NEURAL-NETWORK;
D O I
10.1007/s12205-015-0556-0
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper investigates the comparative performance of wavelet based radial basis networks and multi linear regression in daily reference evapotranspiration estimation. The meteorological data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. The wavelet based radial basis network combines wavelet transformation and radial basis neural network, while the wavelet based regression model combines wavelet transformation and multi linear regression. The results show that the wavelet transformation has significantly positive effects on modeling performance. The wavelet based radial basis network provided the best performance evaluation criteria.
引用
收藏
页码:2050 / 2058
页数:9
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