基于支持向量机的水资源短缺量预测模型及其应用

随着水资源短缺问题的日益突出,利用支持向量机在解决小样本、非线性及高维模式识别问题中的优势,建立了基于支持向量机的水资源短缺影响因子的识别模型和缺水量预测模型,并以北京市为例,对水资源短缺影响因子进行了识别,分析了影响因子的敏感性,进而对北京市未来5年(2014~2018年)的水资源短缺量进行了预测,根据预测结果,为解决北京市水资源短缺问题提出了具体建议。

Establishment and Application of Forecast Model of Water Resources Shortage Based on SVM

The lack of water resource is increasingly serious in recent years. Taking advantage of the priority of SVM in solving small samples, nonlinear and high dimensional pattern recognition, this paper establishes the influencing factor identification model and forecast model of water shortage based on support vector machine (SVM). Taking Beijing City for an example, the influencing factor of water resource shortage is identified and its sensitivity is analyzed. And then it forecasts the water resources deficit of Beijing City in the next five years. Based on the forecast results, some suggestions are put forward to solve the water shortage of Beijing City.