海洋经济增长与海洋艺术产业的可持续协同发展

IF 2.1 4区 地球科学 Q2 MARINE & FRESHWATER BIOLOGY
Zhiping Lian
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引用次数: 0

摘要

海洋与港口经济估算有利于认识海洋经济发展规律。本研究基于先验建筑知识,提出了海洋经济度的神经学习网络估算模型,并采用时间组合类阵列和多元建模方法,对反映浙江省区域海洋经济度发展水平的指标进行了估算。研究采用PK_NN多元建模方法,以货物运输量、货物周转量、近海港口货物运载量、外贸吞吐量、港口集装箱吞吐量、港口集装箱吞吐量为多元模型输入,与其他建模方法相比,GM_11型时间组合类阵列的模型,具有较好的综合性能。最后,利用PK_NN时组合类阵列模型对ZJP地区临近海港2011-2020年海洋经济发展水平进行估算,结果表明ZJP地区海洋经济估算值与ZJP地区实际规划值较为接近。应用该算法估算了ABCDE五个近海港区在不同港首建设艺术模式下的经济度曲线,结果表明,估算的相对值误差控制在4%-10%之间,各月具体增长值区域估算的波动范围相当。这证明了本文基于先验海洋神经学习网络算法的有效性和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sustainable synergistic development of marine economic degree growth and marine art industry

Marine and port economy estimation is conducive to the understanding of the development law of marine economy degree. This study proposes a neural-learning network estimation model of the marine economy degree based on a priori architectural knowledge and adopts time-combined class array columns and multivariate modeling methods to estimate the indicators reflecting the development level of the marine economy degree in the ZJP region. The study adopts the PK_NN multivariate modeling method, taking cargo transportation volume, cargo turnover, cargo carrying value of ports near the sea, foreign trade throughput, port container throughput, and port container throughput as multivariate model inputs, and compared with the other modeling methods, the model of the time combination class array columns of type GM_11, which has a better comprehensive performance. Finally, the PK_NN time-combined class array column model is used to estimate the development level of the marine economy in the ZJP region near the seaport from 2011 to 2020, and the results show that the estimated value of the marine economy in the ZJP region is close to the actual planning value of the ZJP region. The algorithm was applied to estimate the economic degree curves of the five near-seaport areas of ABCDE under different harbor head construction art modes, and the results showed that the relative value error of the estimation was controlled between 4% and 10%, and the fluctuation ranges of each month's specific growth value area estimation were comparable. This proves the effectiveness and accuracy of the a priori marine neural-learning-based network algorithm in this paper.

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来源期刊
Journal of Sea Research
Journal of Sea Research 地学-海洋学
CiteScore
3.20
自引率
5.00%
发文量
86
审稿时长
6-12 weeks
期刊介绍: The Journal of Sea Research is an international and multidisciplinary periodical on marine research, with an emphasis on the functioning of marine ecosystems in coastal and shelf seas, including intertidal, estuarine and brackish environments. As several subdisciplines add to this aim, manuscripts are welcome from the fields of marine biology, marine chemistry, marine sedimentology and physical oceanography, provided they add to the understanding of ecosystem processes.
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