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Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water

by Khaled Ahmadaali, Abdol Majid Liaghat, Nader Heydari, Omid Bozorg Haddad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 6
Year of Publication: 2013
Authors: Khaled Ahmadaali, Abdol Majid Liaghat, Nader Heydari, Omid Bozorg Haddad
10.5120/13250-0715

Khaled Ahmadaali, Abdol Majid Liaghat, Nader Heydari, Omid Bozorg Haddad . Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water. International Journal of Computer Applications. 76, 6 ( August 2013), 12-19. DOI=10.5120/13250-0715

@article{ 10.5120/13250-0715,
author = { Khaled Ahmadaali, Abdol Majid Liaghat, Nader Heydari, Omid Bozorg Haddad },
title = { Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 6 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 12-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number6/13250-0715/ },
doi = { 10.5120/13250-0715 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:45:11.828542+05:30
%A Khaled Ahmadaali
%A Abdol Majid Liaghat
%A Nader Heydari
%A Omid Bozorg Haddad
%T Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 6
%P 12-19
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wheat, barley, sugerbeet, potato, alfalfa, and corn are common crops produced in Iran, which need the most virtual water volume compared to other crops. Determination of the virtual water for these crops would assist in better management of water resources. The main objective of this study is to find out the best technique for estimating and mapping of virtual water. In this research, the virtual water volume was determined by crop water requirement and crop yields using three ANN structures as well as ANFIS technique. Based on RMSE and R2 the comparison of obtained results predicted through the applied ANNs structures indicate that the RBF outperforms the other models for estimating virtual water for wheat, potato, corn, and barley. Moreover, a comparison between RBF and ANFIS revealed that ANFIS is a promising model, which can be efficient mathematical tool for estimation of crop's virtual water.

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Index Terms

Computer Science
Information Sciences

Keywords

virtual water ANN MLP RBF GRNN ANFIS

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