摘要
为提高内河港口规划的合理性,更加精确地预测内河港口集装箱吞吐量,先用安徽芜湖港近年集装箱吞吐量数据建立了灰色GM(1,1)预测模型,然后采用新陈代谢法实时更新预测数据,构建新陈代谢动态灰色GM(1,1)模型,再运用马尔科夫模型分别对两个模型的预测结果进行修正。对比预测结果发现,灰色GM(1,1)马尔科夫模型比灰色GM(1,1)模型平均相对误差降低43%,新陈代谢灰色GM(1,1)马尔科夫模型比新陈代谢灰色GM(1,1)模型平均相对误差降低45%。由此可得出,新陈代谢灰色GM(1,1)马尔科夫模型的结果具有更高的可信度。
In order to improve the rationality of inland river port planning and predict the container throughput more accurately, the Grey GM(1,1)prediction model is established on the recent container throughput data of Wuhu Port in Anhui Province, and the Metabolic Grey GM(1,1) model is constructed by updating the predicted data in real time with the metabolic method. Markov model is used to modify the prediction results of the two models. Between the predicted results, the average relative error of the Grey GM(1,1) Markov model is 43%,lower than that of the Grey GM(1,1) model, and the average relative error of the Metabolic Grey GM(1,1) Markov model is 45%,lower than that of the Metabolic Grey GM(1,1) model.Moreover, the results of Metabolic Grey GM(1,1) Markov have higher reliability.
作者
朱经君
兰培真
徐圣豪
ZHU Jingjun;LAN Peizhen;XU Shenghao(Department of International Business,Anhui Zhong-Ao Institute of Technology,Hefei 23001,China;Navigation College,Jimei University,Xiamen 361021,China;Fujian Chuanzheng Communications College,Fuzhou 350007,China)
出处
《集美大学学报（自然科学版）》
CAS
2022年第4期333-338,共6页
Journal of Jimei University：Natural Science
基金
安徽省高校自然科学重点项目(KJ2021A1506)
安徽省科技创新战略与软科学重点项目(202006f01050024)
安徽省省级质量工程项目(2019XQSXZX69)。
