Solving Water Scarcity of North China Based on Method of Mathematical Modeling
Lin Li, Yifeng Dou, Xueyan Zhang, Guocai Yang
Available Online September 2016.
- https://doi.org/10.2991/iccia-16.2016.40How to use a DOI?
- Water scarcity; GM (1, 1); BP neutral network; Intervention; Regression analysis; Evaluation.
- In this paper, the Logistic model of population forecast, gray theory, neural network model, regression analysis and curve fitting are used to analyze and discuss the region of North China where is in heavily lack of water resources. Firstly, the water demand model and water supply model based on Logistic population prediction and GM (1, 1) are established. Then total water demand in 15 years in the provinces of North China can be judged and calculated using per capita of water requirement. We can draw the conclusion that the total gap in North China is about 35.8 billion cubic meters by MATLAB. Moreover, the result obtained by gray prediction before are tested with the application of Neutral Network Model and shows the result is relatively accurate. Secondly, an intervention plan is designed with the intention of handling water scarcity, which mainly includes building reservoirs and protection of water resources. A mathematical model can be built to determine the pollution degree of four provinces in North China. Regression analysis and curve fitting are used to deal with the forecasting of sewage disposal in the provinces. Finally, the overall strengths and weaknesses of the plan as well as the effect on the surrounding areas and the entire water ecosystem are synthetically discussed.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Lin Li AU - Yifeng Dou AU - Xueyan Zhang AU - Guocai Yang PY - 2016/09 DA - 2016/09 TI - Solving Water Scarcity of North China Based on Method of Mathematical Modeling BT - 2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/iccia-16.2016.40 DO - https://doi.org/10.2991/iccia-16.2016.40 ID - Li2016/09 ER -