最近在藻类繁殖检测和预测技术的进展使用机器学习.
Jungsu Park1, Keval Patel2, Woo Hyoung Lee2
1Department of Civil and Environmental Engineering, Hanbat National University,125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Republic of Korea.
The Science of the total environment
|May 29, 2024
概括
机器学习 (ML) 为检测和预测有害藻类繁殖 (HAB) 提供了先进的解决方案. 这项技术提高了准确性和效率,有助于保护水生生态系统和人类健康.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 有害藻类繁殖 (HABs) 对水生生态系统和人类健康构成重大威胁.
- 传统的检测方法是劳动密集型,昂贵和耗时的.
- 机器学习 (ML) 为改进HAB检测和预测提供了一个有希望的技术进步.
研究的目的:
- 提供ML在HAB检测和预测中的ML应用的全面概述.
- 探索回归,分类和基于图像的ML技术用于HAB分析.
- 突出现实世界的实施和未来的研究方向在ML中用于HAB管理.
主要方法:
- 基于水质和环境因素,利用回归和分类模型进行HAB预测.
- 采用基于图像的ML技术,使用卫星,监视和微观图像来检测藻类.
- 审查了对HAB的ML应用的现有文献和案例研究.
主要成果:
- 机器学习模型在检测和预测HAB方面表现出更高的准确性和效率.
- 基于图像的ML有效地从各种视觉数据源中识别藻类.
- 可解释AI (XAI) 有助于理解HAB的环境驱动因素,提高模型的可解释性.
结论:
- ML技术显著改善了HAB检测和预测,保护生态系统和公共健康.
- 高质量,代表性数据和强大的数据管理对于有效的ML模型性能至关重要.
- 未来的研究应该集中在提高ML模型的适用性和整合XAI,以便在HAB管理中做出明智的决策.
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