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Chinese Journal of Management Science ›› 2020, Vol. 28 ›› Issue (6): 222-230.doi: 10.16381/j.cnki.issn1003-207x.2020.06.021

• Articles • Previous Articles    

Grey Forecasting Model of Rural Water Environment Quality Based on Online Searching Information

ZHANG Ke1,2, ZHONG Qiu-ping1,2, QU Pin-pin1,2, YIN Yao1,2, ZUO Yuan1,2   

  1. 1. School of Business, HoHai University, Nanjing 211100, China;
    2. Institute of Project Management Informatization, Hohai University, Nanjing 211100, China
  • Received:2017-12-04 Revised:2018-10-30 Online:2020-06-20 Published:2020-06-29

Abstract: Water quality prediction is one of the key points in the prevention and control of rural water pollution. However, rural water environment lacks direct monitoring data, and non-direct data is difficult to introduce as variable effectively. A grey forecasting model of rural water environment quality based on online searching information is proposed by integrating grey incidence and prediction model. First, the relationship between rural water quality and online searching information is theorically analyzed, and demonstrated by the online searching data of Lanzhou water pollution event in 2014. Secondly, an initial list of searching keywords associated with rural water environment is collected according to Pressure-State-Response model. Then, by comprehensively contemplating importance and availability of the data, a final list of searching key words is determined. Furthermore, the main features of searching keywords are extracted to construct initial online searching variables by principal component analysis. Thirdly, utilizing grey absolute incidence to evaluate degree of association between each online searching variable and water environment quality. Fourthly, a new multivariable discrete grey forecasting model for different frequency data is proposed. Consequently, strongly associated variables are selected as driving factors of proposed model to construct rural water environment quality model. In case analysis, the data of the Chemical Oxygen Demand (COD) in Wuzhou, Guangxi Zhuang Autonomous Region is collected for 15 weeks. The proposed model is applied to forecast the COD with introducing the internet searching information. The performance of the model is compared with traditional grey forecasting model. The results show that introducing online searching information can significantly improve the accuracy of grey forecasting model of rural water environment quality. This study can provide decision support for rural water environment management. Meanwhile, a new approach for prediction of rural water environment is established.

Key words: rural water environment, online searching information, absolute degree of incidence, grey model, multi-variables

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