Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models-Reference-Cited by-同舟云学术

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Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models

Published:2018-07-24 Issue:4 Volume:67 Page:615-624
ISSN:1531-0353
Container-title:Irrigation and Drainage
language:en
Short-container-title:Irrig. and Drain.

Author:

Qi Yanbing¹²,Huo Zailin¹,Feng Shaoyuan³,Adeloye Adebayo J.⁴,Dai Xiaoqin⁵

Affiliation:

1. Centre for Agricultural Water Research in China; China Agricultural University; Beijing PR China

2. Beijing Water Science & Technology Institute; Beijing PR China

3. School of Hydraulic, Energy and Power Engineering; Yangzhou University; Yangzhou Jiangsu PR China

4. Institute for Infrastructure and Environment; Heriot-Watt University Edinburgh United Kingdom

5. Institute of Geographic Sciences and Natural Resources Research; Chinese Academy of Science Beijing PR China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Wiley

Subject

Soil Science,Agronomy and Crop Science

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/ird.2270/fullpdf

Reference42 articles.

1. The relative utility of multiple regression and ANN models for rapidly predicting the capacity of water supply reservoirs;Adeloye;Environmental Modelling & Software,2009

2. Neural networks based generalised models for reservoir capacity-yield-reliability behaviour;Adeloye;Journal of Hydrology,2006

3. Neural computing of the reference crop evapotranspiration;Adeloye;Environmental Modelling & Software,2012

4. Effect of deficit irrigation on yield, water productivity, and economic returns of wheat;Ali;Agricultural Water Management,2007

5. Evapotranspiration information reporting: I. Factors governing measurement accuracy;Allen;Agricultural Water Management,2011a

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