Emergency resources demand forecast based on FCM and CBR-GRA dual search

Multi-data analysis and reasoning techniques were adopted to improve the forecasting speed and reliability of emergency resources demand. Firstly, based on the historical case information, the rescue case index weights were calculated. Then an algorithm combining fuzzy C-means clustering with case retrieval was established to increase the efficiency of case retrieval, which was performed by CBR (casebased reason) similarity and GRA (grey relational analysis) correlation. After the CBR similarity vector and GRA correlation vector were obtained, the grey relational analysis was used to calculate the similarity-correlation vector so as to ensure that similar cases are retrieved efficiently. Finally, a resources demand model was built up. The results confirmed that case clustering to achieve preliminary data filtering can enhance retrieval speed and combining two retrieval methods can improve the reliability of retrieval.