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
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;
Conference_Titel :
Smart and Sustainable City 2013 (ICSSC 2013), IET International Conference on
Conference_Location :
Shanghai
Electronic_ISBN :
978-1-84919-707-6
DOI :
10.1049/cp.2013.2011