摘要
科学预判长三角港口群的发展趋势对加快建设交通强国、海洋强国的意义重大,而港口货物吞吐量的精准预测是促进港口发展的重要一点。为了提高预测的精准度,采取BP神经网络算法、灰色预测GM(1,1)模型和三次指数平滑法对2009-2021年长三角港口群的宁波舟山港、上海港、苏州港三大港口货物吞吐量进行分析,建立组合预测模型。结果显示,该组合预测模型降低了预测误差,提高了预测精准度,对长三角港口群发展具有一定的参考价值。
Scientific prediction of the development trend of the Yangtze River Delta port group is of great significance for accelerating the construction of a transportation and maritime power,and the accurate and scientific prediction of port cargo throughput is an important factor in promoting port development.In order to improve the accuracy of prediction,this paper adopts BP neural network algorithm,grey prediction GM(1,1)model and cubic exponential smoothing method to analyze the cargo throughput of Port of Ningbo-Zhoushan,Port of Shanghai and Port of Suzhou in the Yangtze River Delta port group from 2009 to 2021,and establishes a combined prediction model.The results show that the combined prediction model reduces prediction errors and improves prediction accuracy,which can provide certain reference value for the development of the Yangtze River Delta port group.
作者
邹荣妹
兰国辉
杨霞
Zou Rongmei;Lan Guohui;Yang Xia(Anhui University of Science&Technology,Huainan 232001,China;Huainan Normal University,Huainan 232038,China)
出处
《廊坊师范学院学报（自然科学版）》
2024年第1期92-99,共8页
Journal of Langfang Normal University(Natural Science Edition)
基金
安徽省教学研究重点项目“新工科背景下产学研协同育人模式与机制研究”(2021jyxm0351)
淮南市科技研发项目“淮南市科技创新助力乡村振兴路径研究”(2021A244)
2021年度安徽高校人文社会科学研究项目“基于CAS理论的高校安全管理风险识别及协同治理机制研究—以淮南师范学院为例”(SK2021A0543)。
