针对小区域内历史灾情资料不多的特点，引入了信息扩散的模糊数学方法，对历史灾情资料进行优化处理，提供了对农业自然灾害进行风险评估的实用方未能。在“湖南省自然灾害风险监测管理信息系统”中的应用表明，该方法给出的结果同实际情况相当吻合。

The method of the information distribution is used to produce a more intelligent architecture, called primary information matrix. Then, without any assumption, we can construct a fuzzy relation matrixf or fuzzy inference. To display the advantage of the new framework, in this paper, we use it to study the relationship between epicentre intensity, I0, and earthquake magnitude, M. The result shows that the new model is better than the traditional regression model.

介绍了风险研究的基本内容，探讨了自然灾害风险分析的基本原理，从风险的本质和自然灾害的具体表现可以看出，自然灾害风险的分析，依赖于一整套贯穿于致灾因子到灾害影响的、能前后相联的模型，本文对各个环节的模型提出了一些原则性的建议。

In this paper, we use the information matrix technique to extract fuzzy if-then rules from data including noise. Witha normal diffusion function, we change all crisp observations of a given sample into fuzzy sets to make an information matrix. We extract rules according to the centroids of the rows of an information matrix. These rules are integrated into an additive fuzzy system with the same rule weight. Such fuzzy systems can be used as adaptive function approximators. Simulations show that this method is very effective compared with the conventional least-squares method and neural network. The best advantage of the suggested method is that, it may be the simplest way to extract fuzzy if-then rules from data.

In this paper, we use the interior-outer-set model to calculate the risk of crop flood and order farming alternatives for Huarong County, China, where only a small sample of eight observations is available.Any risk assessment from the data must be imprecise. The risk calculated by this suggested model is a particular case among imprecise probabilities, called possibility-probability distribution.We discuss in detail how to order alternatives based on a possibility-probability distribution.The result shows, aside subjective assessment, the fuzzy risk can also be calculated.The comparison shows that the ordering based on a calculated fuzzy risk is better than one based on a histogram estimate.