基于灰色神经网络模型的水资源生态足迹预测——以广西为例

水资源利用评价与趋势预测是当前水资源研究的热点问题。运用生态足迹方法计算了广西1997~2014年的水资源生态足迹、水资源生态承载力和水资源生态盈余,在此基础上,采用灰色神经网络模型进行动态模拟并预测其2015~2019年的发展趋势。结果表明:1 1997~2014年,广西人均水资源生态足迹和水资源生态承载力总体均呈下降态势,但前者的降幅明显小于后者;历年水资源均表现为生态盈余但总体呈下降走势,表明该地区水资源利用处于可持续状态但面临逐渐转向不可持续的威胁。2 2015~2019年的人均水资源生态足迹将维持在0.9~1.1 hm~2左右,其走向是先升后降;人均水资源生态承载力将保持在1.8~2.3hm~2左右,波动明显;人均水资源生态盈余介于0.7~1.3 hm~2之间,水资源利用仍将处于可持续状态,但可持续开发利用空间相较之前明显缩小。3与常用的灰色模型相比,灰色神经网络模型模拟精度具备明显优势并具有很强的内插拟合能力和较好的外推预测能力,可应用于同类问题的预测分析。

Prediction of water resources ecological footprint based on Grey Neural Network Model: case of Guangxi Zhuang Autonomous Region

Assessment and trend prediction of water resources sustainable utilization is a hot issue. This paper uses ecological footprint method to calculate water resources ecological footprint, water resources ecological carrying capacity and water resources ecological surplus of Guangxi from1997 to 2014. On the basis of the calculation result, Grey Neural Network Model was used to dynamically simulate and predict the trend of 2015-2019. The results shows that: (1) from 1997 to 2014, the water resources ecological footprint and carrying capacity per capita in Guangxi Zhuang Autonomous Region showed a downward trend, but the decline of the water resources ecological footprint was significantly less than that of the carrying capacity; the water resources had ecological surplus but an overall downward trend, which showed the water resources utilization was sustainable but gradually became unsustainable. (2) The water resources ecological footprint per capita from 2015~2019 will maintain at about 0.9~1.1 hm2, the trend will be upward and then downward; the water resources ecological carrying capacity per capita will remain around 1.8~2.3 hm2, and the fluctuation is obvious; the water resources ecological surplus per capita will be around 0.7~1.3 hm2, and the water resources utilization will still be in the state of sustainable development, but the space of sustainable development and utilization will significantly reduce compared with the previous years. (3) Compared with the common Grey Model, Grey Neural Network Model simulation accuracy has obvious advantages, strong interpolated fitting ability and good extrapolation forecast ability, which can be used to forecast and analyze the similar issues.