RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN >> 2016, Vol. 25 >> Issue (09): 1317-1327.doi: 10.11870/cjlyzyyhj201609002

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VULNERABILITY PREDICTION OF BASIN WATER RESOURCES BASED ON ROUGH SET AND BP NEURAL NETWORK——A CASE OF HUAIHE BASIN

LIU Qian-qian, CHEN Yan   

  1. School of Economics and Management, Nanjing Forestry University, Nanjing 210037, China
  • Received:2015-12-21 Revised:2016-02-19 Online:2016-09-20
  • Supported by:
    National Natural Foundation for young scholar (71403122)(BK20140980);Natural Foundation of Jiangsu Province for young scholar (BK20140980)(14YJC630018);Humanities and Social Science Foundation of Chinese Ministry of Education for young scholar (14YJC630018)

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Abstract: Water is an important natural and economic resource. Predicting its vulnerability can forecast the water security and give an early warning of its vulnerability in given areas. Therefore, reasonable and scientific water resource vulnerability prediction research is an effective method to alleviate vulnerability. At present, water resource vulnerability studies mainly aimed at evaluating current situation of water resource vulnerability, while there is little work about forecasting. In this paper we combined the rough set and BP neural network to make attribute reduction of the evaluation indices system by using improved data analysis. Then, we established the forecast model by fitting functional relationship between index data and water vulnerability index based on the BP neural network. We discussed on Huaihe Basin's future water resource vulnerability situation based on the previous research. The results showed that, the WVI of Huaihe Basin in 2005, 2020 and 2025 were 0.305, 0.359 and 0.390, which were lightly and moderately vulnerable. Except for 2015, the water resource vulnerability in 2020 and 2025 was more seriously compared with recent years. According to the index data we revealed the main causes for this phenomenon to be annual precipitation, water per capita, GDP wastewater discharge, cultivation index, effective irrigation area ratio, and drought area ratio. In order to avoid the increasing water resource vulnerability the relevant departments should strengthen targeted management and control of these aspects.

Key words: Basin Water Resources, Vulnerability Prediction, Rough Set, BP neural network, Huaihe Basin

CLC Number: 

  • TV213
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