دورية أكاديمية
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Ocean carbon emission prediction and management measures based on artificial intelligence remote sensing estimation in the context of carbon neutrality.

التفاصيل البيبلوغرافية
العنوان: Ocean carbon emission prediction and management measures based on artificial intelligence remote sensing estimation in the context of carbon neutrality.
المؤلفون: Wang B; School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China. Electronic address: wangbin@just.edu.cn., Hua L; CMA Earth System Modeling and Prediction Centre, Beijing 100081, China; State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, China; Key Laboratory of Earth System Modeling and Prediction China Meteorological Administration, Beijing 100081, China., Al-Mohaimeed AM; Department of Chemistry, College of Science, King Saud University, P.O. Box 22452, Riyadh 11495, Saudi Arabia., Zhao N; Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokosuka, Japan.
المصدر: Environmental research [Environ Res] 2024 Jun 15; Vol. 251 (Pt 1), pp. 118591. Date of Electronic Publication: 2024 Feb 29.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0147621 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-0953 (Electronic) Linking ISSN: 00139351 NLM ISO Abbreviation: Environ Res Subsets: MEDLINE
أسماء مطبوعة: Publication: <2000- > : Amsterdam : Elsevier
Original Publication: New York, Academic Press.
مواضيع طبية MeSH: Remote Sensing Technology*/methods , Environmental Monitoring*/methods , Oceans and Seas* , Carbon*/analysis , Artificial Intelligence*, China
مستخلص: With rapid economic development, the gradual deterioration of the natural environment has posed unprecedented challenges to human social civilization. The marine economy, as an important part of economic development, is the breakthrough of economic transformation for many coastal countries. Additionally, green development and environmental impact assessment have become the focus of research in these countries. This study employs remote sensing technology, an efficient observational method, to significantly enhance the efficiency of ocean information observation. It investigates ocean carbon emissions within the framework of carbon neutrality. First, we identified the ships along the coastline based on marine remote sensing information through the YOLO (you only look once) framework. Second, we applied the LSTM (long short-term memory) method to combine the target identification results and the historical data of carbon emissions to complete the corresponding carbon emission data fitting. Finally, carbon emission data from the past three years in the offshore area of Dalian were used to make accurate predictions. The results suggested that the recognition rate of the proposed target detection method could reach 88%, and the LSTM method has shown the best performance in terms of absolute error for the subsequent short-term carbon emission prediction. This framework not only provides essential technical support for analyzing remote sensing information within the context of carbon neutrality but also introduces innovative insights for carbon emission prediction.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Inc. All rights reserved.)
فهرسة مساهمة: Keywords: Environment evaluation; Machine learning; Ocean carbon emission; Remote sensing; YOLO
المشرفين على المادة: 7440-44-0 (Carbon)
تواريخ الأحداث: Date Created: 20240301 Date Completed: 20240601 Latest Revision: 20240601
رمز التحديث: 20240602
DOI: 10.1016/j.envres.2024.118591
PMID: 38428561
قاعدة البيانات: MEDLINE
الوصف
تدمد:1096-0953
DOI:10.1016/j.envres.2024.118591