AIS-based operational phase identification using Progressive Ablation Feature Selection with machine learning for improving ship emission estimates-Reference-Cited by-同舟云学术

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AIS-based operational phase identification using Progressive Ablation Feature Selection with machine learning for improving ship emission estimates

Published:2024-01-22 Issue:2 Volume:74 Page:100-115
ISSN:1096-2247
Container-title:Journal of the Air & Waste Management Association
language:en
Short-container-title:Journal of the Air & Waste Management Association

Author:

Duan Kuiquan¹,Li Qingbo¹^ORCID,Liu Shangheng¹,Liu Yanxin¹,Wang Shuang¹,Li Shuang¹,Wang Xiaochuan²,Ma Nan³,Ma Ye¹

Affiliation:

1. Green Shipping and Carbon Neutrality Laboratory, College of Environmental Science and Engineering, Dalian Maritime University, Dalian, People’s Republic of China

2. Danger Prevention Division, Qinhuangdao Maritime Safety Administration of People’s Republic of China, Qinhuangdao, People’s Republic of China

3. Transportation Safety Research Center, China Academy of Transportation Sciences, Beijing, People’s Republic of China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10962247.2023.2274348

Reference48 articles.

1. Transfer sea level learning in the Bosphorus Strait by wavelet based machine learning methods

2. A survey on feature selection methods

3. High-spatiotemporal-resolution ship emission inventory of China based on AIS data in 2014

4. Estimating ship emissions based on AIS data for port of Tianjin, China

5. An operational-mode-based method for estimating ship emissions in port waters

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