A context-dependent knowledge model for evaluation of regional environment

In this paper we develop a rule-based model for evaluation of regional environment based on both hard and soft data, where by hard data we mean the statistical measurements while by soft data we mean subjective appreciation of human beings of environmental issues. As people's feeling strongly depends on the social and economical characteristics of administrative regions where they live, we firstly use the hard data concerning these characteristics to do clustering in order to obtain clusters corresponding to regions with the homogeneous social and economical characteristics relatively. We then use the soft data, with the help of data-mining techniques, to develop rule-based models which show association between evaluated items of residents in the clusters. Finally, a relationship between hard data and soft data through an integrated model will be explored. It is shown that the soft data are rather reliable and we should integrate subjective knowledge learnt from soft data into modelling of environmental issues.