Application of AHP and Entropy to the Case Based Reasoning of Flood Disasters
应用于洪涝灾害案例推理中的层次熵分析法

The key factor that affects the case retrieval matching in Case-based reasoning is the distribution and determination of characteristic attribute weights. In this paper, AHP and Entropy was applied in the Case-based reasoning on flood disaster to estimate the need for disaster relief food rations. AHP was used to analyze and determine the variety of factors which lead to construct a reasonable hierarchy, and get the priority of attribute weights through combining the information of subjective judgments. On this basis, using the entropy technology to modify the priority, and ensure the objectivity of characteristic attribute weights to make the preferred cases fit the reality better. Experimental results show that this method can be applied to the case-based reasoning of floods disaster, and through this method, the best case for the target case can be selected from a number of historical cases. In this way, the scientific estimation of the needs for disaster relief rations can be guaranteed.