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基于人工智能遥感估计的海洋碳排放预测和管理措施,以碳中和为背景
Bin Wang1, Lijuan Hua2, Amal M Al-Mohaimeed3
1School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Environmental research
|March 1, 2024
概括
这项研究使用遥感和人工智能跟踪船只和预测海洋碳排放,帮助实现碳中和目标. YOLO框架实现了88%的识别率,LSTM提供了准确的短期排放预测.
科学领域:
- 海洋生物学 海洋生物学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 快速的经济发展导致环境退化,特别是在沿海地区.
- 海洋经济对沿海国家至关重要,需要绿色发展和环境影响评估.
- 在碳中和的更广泛背景下,海洋碳排放是一个重大问题.
研究的目的:
- 通过遥感技术提高海洋信息观测效率.
- 在碳中和的框架内调查海洋碳排放.
- 开发一个海上碳排放的预测模型.
主要方法:
- 沿海岸线使用海洋遥感和YOLO (你只看一次) 框架进行船舶识别.
- 碳排放数据通过应用LSTM (长短期记忆) 方法来匹配,以将目标识别与历史碳排放数据相结合.
- 短期碳排放预测使用3年的离岸大连数据.
主要成果:
- 通过YOLO框架实现了88%的船舶识别识别率.
- 在短期碳排放预测中,LSTM方法在绝对误差方面表现优越.
- 开发的框架为碳中和倡议中的遥感分析提供了技术支持.
结论:
- 综合遥感和人工智能方法有效监测和预测海洋碳排放.
- 该方法为碳排放预测提供了创新的见解,并支持环境管理.
- 该研究为分析碳中和目标的海洋遥感数据提供了一个强大的框架.
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