Artificial neural networks approach in evapotranspiration modeling: a review

被引:182
作者
Kumar, M. [1 ]
Raghuwanshi, N. S. [2 ]
Singh, R. [2 ]
机构
[1] Cent Res Inst Dryland Agr, Div Resource Management, Hyderabad 500059, Andhra Pradesh, India
[2] Indian Inst Technol, Agr & Food Engn Dept, Kharagpur 721302, W Bengal, India
关键词
DAILY PAN EVAPORATION; EQUATIONS; WATER;
D O I
10.1007/s00271-010-0230-8
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
The use of artificial neural networks (ANNs) in estimation of evapotranspiration has received enormous interest in the present decade. Several methodologies have been reported in the literature to realize the ANN modeling of evapotranspiration process. The present review discusses these methodologies including ANN architecture development, selection of training algorithm, and performance criteria. The paper also discusses the future research needs in ANN modeling of evapotranspiration to establish this methodology as an alternative to the existing methods of evapotranspiration estimation.
引用
收藏
页码:11 / 25
页数:15
相关论文
共 56 条
[1]  
Berry MichaelJ., 1997, DATA MINING TECHNIQU
[2]  
Bruton JM, 2000, T ASAE, V43, P491, DOI 10.13031/2013.2730
[3]   River flood forecasting with a neural network model [J].
Campolo, M ;
Andreussi, P ;
Soldati, A .
WATER RESOURCES RESEARCH, 1999, 35 (04) :1191-1197
[4]   Estimation, forecasting and extrapolation of river flows by artificial neural networks [J].
Cigizoglu, HK .
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2003, 48 (03) :349-361
[5]  
Cochran W.G. G.M. Cox., 1957, Experimental Design
[6]   Artificial neural network models for estimating regional reference evapotranspiration based on climate factors [J].
Dai, Xiaoqin ;
Shi, Haibin ;
Li, Yunsheng ;
Ouyang, Zhu ;
Huo, Zailin .
HYDROLOGICAL PROCESSES, 2009, 23 (03) :442-450
[7]   An artificial neural network approach to rainfall-runoff modelling [J].
Dawson, CW ;
Wilby, R .
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 1998, 43 (01) :47-66
[8]   RAINFALL FORECASTING IN SPACE AND TIME USING A NEURAL NETWORK [J].
FRENCH, MN ;
KRAJEWSKI, WF ;
CUYKENDALL, RR .
JOURNAL OF HYDROLOGY, 1992, 137 (1-4) :1-31
[9]  
Gomez KA, 1984, STAT PROCEDURES AGR
[10]  
Govindaraju RS, 2000, J HYDROL ENG, V5, P124