Open AccessPrediction of Chinese Port Cargo Volume Based on BP Algorithm
Author(s) -
Chenwei Zhong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1982/1/012168
Subject(s) - port (circuit theory) , volume (thermodynamics) , china , measure (data warehouse) , artificial neural network , computer science , predictive power , operations research , marine engineering , data mining , engineering , artificial intelligence , geography , philosophy , physics , archaeology , epistemology , quantum mechanics , electrical engineering
With the implementation of maritime power strategy, China attaches more and more importance to port economy. As a powerful indicator to measure Marine economy, port cargo volume can reflect whether the effectiveness of port construction in China is remarkable at the present stage. In this study, the author takes the cargo volume data of China’s ports from 2010 to 2017 as the training data, and predicts the cargo volume of a January in 2018 and 2019 based on the time series method combined with BP neural network to determine the feasibility of the prediction model.