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Urban water consumption has some characteristics of grey because it is influenced by economy, population, standard of living and so on. The multi-variable grey model (MGM(1,n)), as the expansion and complement of GM(1,1) model, reveals the relationship between restriction and stimulation among variables, and the genetic algorithm has the whole optimal and parallel characteristics. In this paper, the parameter q of MGM(1,n) model was optimized, and a multi-variable grey model (MGM(1,n,q)) was built by using the genetic algorithm. The model was validated by examining the urban water consumption from 1990 to 2003 in Dalian City. The result indicated that the multi-variable grey model (MGM(1,n,q)) based on genetic algorithm was better than MGM(1,n) model, and the MGM(1,n) model was better than MGM(1,1) model.
Urban multi-variable gray model (MGM (1, n)), as the expansion and complement of GM (1, 1 ) model, reveals the relationship between restriction and stimulation among variables, and the genetic algorithm has the whole optimal and parallel characteristics. In this paper, the parameter q of MGM (1, n) model was optimized, and a multi-variable gray model The model was validated by examining the urban water consumption from 1990 to 2003 in Dalian City. The result indicated that the multi-variable gray model (MGM (1, n, q)) was built by using the genetic algorithm. , n, q)) based on genetic algorithm was better than MGM (1, n) model, and the MGM (1, n) model was better than MGM (1,1) model.