整合遥感和机器学习来检测水电水库的度异常
Anderson P Souza1, Bruno A Oliveira2, Mauren L Andrade3
1SIMOA - Intelligent Systems for Environmental Monitoring, Department of Sanitary and Environmental Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
The Science of the total environment
|August 4, 2023
概括
机器学习和遥感有效地检测水库表面水中的高度事件. 这种方法有助于通过识别水质异常来监测水生生态系统和管理水资源.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 水库水质监测对于水生生态系统和社会经济服务至关重要.
- 度等物理化学参数的突然变化可以表明影响水资源的关键事件.
- 有效的水资源管理需要及时识别这些异常.
研究的目的:
- 整合机器学习 (ML) 异常检测与遥感数据.
- 为了识别特雷斯马里亚斯水电水库的表面水中的高度事件.
- 评估ML模型在诊断水质异常方面的性能.
主要方法:
- 利用遥感图像进行数据采集.
- 应用专门的机器学习算法用于异常检测.
- 评估了四种不同的基于值的场景,以确定度事件.
- 使用F1得分指标评估模型性能.
主要成果:
- 使用ML模型生成异常识别地图.
- 通过F1得分来衡量的表现,随着更严格的门而下降.
- 这些模型成功地确定了具有异常度值的位置.
- 这种方法在不同的水文环境 (干旱和潮湿季节) 中被证明是有效的.
结论:
- ML异常检测和遥感的整合适用于在水库中诊断地表水质.
- 开发的方法有效地识别了高度事件,有助于水资源管理.
- 该研究强调了这些技术在各种水文条件下的实用性.
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