基于案例推理的非常规突发事件资源需求预测

Forecasting Model of Unconventional Emergence Incident's Resource Demand Based on Case-based Reasoning

近年来非常规突发事件频繁发生,严重危害了人民的生命财产安全,而如何及时的预测灾害发生后的资源需求成为一个重要课题。文章提出了一种精确的预测方法,将模糊集理论,神经网络Hebb学习规则和多元线性回归与案例推理法相结合。这种方法很好地解决了非常规突发事件资源需求预测这类信息不完备、不精确问题,能够比较准确的做出资源的需求预测。该模型对灾害资源需求预测具有一定的参考价值。

In recent years the unconventional emergence incident occurred frequently,which is seriously harmful to people’s lives and property.So how to predict resource requirements after disasters timely becomes an important issue.This paper presents an accurate prediction method--case-based reasoning,combined with fuzzy set theory,neural network Hebb learning rule and multiple linear regression.This approach can make quite accurate resources demand forecasts for this kind of information incomplete,not precise question.At the same time,this model has some reference value to the prediction of the disaster resource demand.