基于云模型和差异驱动原理的农用地分等研究

Agricultural land classification based on the cloud models and the principle of difference drive

在农用地分等的过程中,评价因素涵盖了定量和定性的数据,定量数据准确地反映该因子的特征,而定性因素则无法用准确的数据来表示的。语言值或者概念和数学符号的最大区别,就是其中的不确定性。此外专家打分确定的权重也存在随机性,各评价因素对农用地等别影响的侧重也不能得到确切的再现。针对以上理由,本文提出采用云模型的不确定性分析和基于差异驱动原理的赋权,试图改进现行的农用地分等方法中因素作用的判读及其对分等结果的影响。

Both quantitative and qualitative data are contained in the process of agricultural land classification. Quantitative data can reflect the characteristics of the factor accurately, but the qualitative factors can not be expressed with accurate data. Uncertainty is the biggest difference between language or concept and mathematical symbol. Moreover, there exists randomicity by using expert grade method. The effect for each evaluation factor on the grading of agricultural land can not be reproduced. For the reasons mentioned above, this paper presents a cloud model - based uncertainty analysis and difference drive principle-based weight method to improve the existing agricuhural land classification method.