基于粗糙集和CBR的救灾口粮需求预测

Relief Food Demand Forecast Based on Rough Set and Case-based Reasoning

救灾口粮预测所采用的方法多以专家经验判断为主,具有较大的随机性。为此,从灾害案例的特点出发,针对案例推理时存在效率低下和权重确定差异性较大的问题,结合粗糙集处理不确定知识的优点和案例推理的特点,提出一种方法,实现灾害应急救灾口粮需求预测,并通过洪涝灾害实例进行分析。结果表明该方法有利于减少主观影响,提高需求预测的准确率和效率。

The methods for forecasting relief food, which are based on the experts and the prediction results, are influenced by the experience of the experts. Considering the advantage of rough set in dealing with the uncertain knowledge, this paper proposes a novel method that combines rough set with Case-based Reasoning（CBR） for forecasting relief food demand. This method utilizes discernibility matrix and attributes significance to reduce case attributes and get attribute weights. Experimental results prove that the proposed method can reduce the subjective effects and improve the predication accuracy.