With the economic development, the demand for water resources has been increasing dramatically during the last several decades. The sustainable development of water resources has become a major challenge in our society. As the largest economic center in west China, Chongqing was chosen as a typical unit to investigate this issue by using statistical data of fifteen years. In this study, the complexity of the water resource system was simplified through hierarchical structure analysis. Then, grey relational analysis was used to measure hierarchical correlation degree. The correlation between the levels of water consumption was analyzed, especially between water consumption and socio-economic indicators. Based on the result of hierarchical grey evaluation, three conclusions were drawn: (1) from the water consumption-oriented aspect, the correlation rankings, from high to low, are production water use, domestic water use, and eco-environmental water use respectively; (2) from industrial structure aspect, the secondary industry has the highest grey relational degree, which is followed by the primary industry (agriculture); and (3) from the economic and social indicators aspect, many significant factors are highly related to water consumption, such as precipitation, urbanization rate, total population, GDP, the proportion of output value of the three industries and residential water price. In this paper, to achieve the goal of the strictest system of water resource management during the 13th Five-Year Period, the corresponding policy suggestions are proposed for the municipal government of Chongqing.
A stable electricity supply is the basis for ensuring the healthy and sustained development of a regional economy. Reasonable electricity prediction is the key to guaranteeing the stability and efficiency of electricity supply. To this end, we used a reformative grey prediction model to forecast electricity demand. In order to effectively improve the smoothness of a raw modelling sequence, we employed an existing smoothing algorithm that significantly compressed the amplitude of the random oscillation sequence. Then, an improved grey forecasting model with three parameters (IGFM_TP) was deduced. In the end, a new model was used to forecast the demand for electricity of one city in the western region of China, and comparisons of simulation values and errors with those of GFM_TP, GM(1,1), DGM(1,1) and SAIGM were conducted. The findings show that the mean absolute simulation percentage error of IGFM_TP was 7.8%, and those of the other four models were 12.1%, 12.3%, 11.1%, and 10.1%, respectively. Therefore, the simulation precision of the new model achieved an optimal effect. The proposed new grey model provides is an effective method for electricity demand prediction.
Grey prediction models have become common methods which are widely employed to solve the problems with “small examples and poor information.” However, modeling objects of existing grey prediction models are limited to the homogenous data sequences which only contain the same data type. This paper studies the methodology of building prediction models of interval grey numbers that are grey heterogeneous data sequence, with a real parameter. Firstly, the position of the real parameter in an interval grey number sequence is discussed, and the real number is expanded into an interval grey number by adopting the method of grey generation. On this basis, a prediction model of interval grey number with a real parameter is deduced and built. Finally, this novel model is successfully applied to forecast the concentration of organic pollutant DDT in the atmosphere. The analysis and research results in this paper extend the object of grey prediction from homogenous data sequence to grey heterogeneous data sequence. Those research findings are of positive significance in terms of enriching and improving the theory system of grey prediction models.
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