改进可变模糊算法在水资源承载力评价中的应用

针对可变模糊算法在评价指标体系构建时没有考虑指标间的相关性对评价结果造成的影响,以及该算法在水资源承载力评价时没有对研究区域水资源承载力的时序变化特征进行探讨,引入了主分量分析(PCA)法以筛选出彼此间相关系数较低的指标来构建评价指标体系。因此对可变模糊算法进行了改进,旨在分析不同年份下的样本水资源承载状况的时序变化特征;同时,以大同市为例,对该地区(2010～2017年)的水资源承载状况进行评估。评估结果表明:改进后的算法与改进前的相比,在维持评价等级不变的前提下,级别特征值的稳定范围明显缩小,减少了误差范围,提高了评价等级精度。该改进算法进一步拓展和丰富了可变模糊集理论,可为后续评定其他区域的水资源承载状况提供一定的参考。

Application of modified variable fuzzy algorithm in evaluation of water resources carrying capacity

In variable fuzzy algorithm, the evaluation index system is constructed without consideration on influence of the correlation among indexes on the evaluation results and without analysis on the time series variation characteristics of the water resources carrying capacity in the evaluation. To overcome the shortcoming, Principal Component Analysis (PCA) was introduced to select the indexes with low correlation coefficient to construct the evaluation index system. The variable fuzzy algorithm was improved to analyze the time-series variation characteristics of the water resources carrying status in different years. Taking Datong City, Shanxi Province as an example, this paper evaluates the water resources carrying capacity of the city from 2010 to 2017. The evaluation results show that compared with the original algorithm, the stability range of the grade characteristic value calculated by improved algorithm is obviously reduced if under the same evaluation grade and the range of error is reduced and the accuracy of the evaluation grade is improved. The improved algorithm further expands and enriches the variable fuzzy set theory, which has some reference significance for evaluating the water resources carrying status in other regions by the variable fuzzy algorithm.