Agricultural land classification based on the cloud models and the principle of difference drive
Luo Shao-feng · Cehui kexue · 2010
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 agricultural land classification method.