The Impact of Improving Throughput Efficiency of Coastal Ports Based on Machine Learning Methods on Sulfur Emissions

DOI

10.2991/978-94-6463-516-4_36How to use a DOI?

Keywords

Port throughput; Night light remote sensing images; Sulfur emissions; LSTM

Abstract

This article uses the LSTM long short-term memory model to predict the throughput of major coastal ports in China and remote sensing images of port night light, and explores the correlation between remote sensing images of port night light and sulfur emissions. Research has shown that the cargo throughput of major coastal ports in China increased at a rate of 5% from 2019 to 2029, which is significantly positively correlated with the growth of nighttime light data. The growth of nighttime light data is negatively correlated with the decrease in sulfur emissions in ports. This study can provide new ideas for the future green development of ports, thermal environment management.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xumeng Wang
PY  - 2024
DA  - 2024/09/17
TI  - The Impact of Improving Throughput Efficiency of Coastal Ports Based on Machine Learning Methods on Sulfur Emissions
BT  - Proceedings of the 2024 5th International Conference on Urban Construction and Management Engineering (ICUCME 2024)
PB  - Atlantis Press
SP  - 343
EP  - 350
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-516-4_36
DO  - 10.2991/978-94-6463-516-4_36
ID  - Wang2024
ER  -