DocumentCode
681272
Title
GANN-based prediction of fresh water resources
Author
Cuiyun Gao ; Linbo Jin ; Wanggen Wan ; Rui Wang
Author_Institution
Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China
fYear
2013
fDate
19-20 Aug. 2013
Firstpage
79
Lastpage
83
Abstract
The issue of fresh water resources which has limited development in majority places is one of the most concerned focuses these days. This paper provides a novel approach to designing a specific and rational strategy for prediction of fresh water resources nationwide. The statistics across China are from Official Web sites. By comparing different methods including GM (1, 1), Logistic Regression Model and BP Neural Network, we establish a novel method named GANN which combines the strengths of GM and BP. Besides, WSI (Water Shortage Index) is created to represent the degree of water shortage. Also, experiments of different places are presented in our paper to prove our method.
Keywords
backpropagation; environmental science computing; grey systems; neural nets; regression analysis; water resources; BP neural network; China; GANN-based fresh water resources prediction; GM (1, 1) method; WSI; logistic regression model; statistical analysis; water shortage degree; water shortage index; BP; Freshwater Withdrawals; GANN; Water Production Capacity; Water Shortage Index;
fLanguage
English
Publisher
iet
Conference_Titel
Smart and Sustainable City 2013 (ICSSC 2013), IET International Conference on
Conference_Location
Shanghai
Electronic_ISBN
978-1-84919-707-6
Type
conf
DOI
10.1049/cp.2013.2011
Filename
6737793
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