Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)

An integrated prediction model for water supply-demand ability

Authors
Xiaozhu Jing
Corresponding Author
Xiaozhu Jing
Available Online September 2016.
DOI
https://doi.org/10.2991/amitp-16.2016.105How to use a DOI?
Keywords
supply and demand ,simple polynomial regression prediction ,grey prediction,
Abstract
In this paper, a model is developed to measure the ability of water supply in a region, based on the dynamic nature of the factors that affect both supply and demand. We pick China, a water-strapped country, to begin our analysis. For water demand, we assume the water demand can be approximately thought as the water withdrawal consumption. The withdrawal is divided into three parts: agricultural, industrial and municipal. For water supply, we mainly consider surface water, underground water and desalination. Through the historical data from China, we get the fitting curves. Using simple polynomial regression prediction and grey prediction, we establish the model to predict the water withdrawal in the future. As for water supply, we build model by using linear regression prediction and polynomial prediction.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
Part of series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-245-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/amitp-16.2016.105How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiaozhu Jing
PY  - 2016/09
DA  - 2016/09
TI  - An integrated prediction model for water supply-demand ability
BT  - 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/amitp-16.2016.105
DO  - https://doi.org/10.2991/amitp-16.2016.105
ID  - Jing2016/09
ER  -