基于机器学习模型和基于集群分析的经验模型的水质变量估计,使用内陆水库的多来源遥感数据,中国南方
Di Tian1, Xinfeng Zhao2, Lei Gao3
1School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China.
Environmental pollution (Barking, Essex : 1987)
|December 9, 2023
概括
这项研究利用无人机和卫星数据加强了水库水质监测. 机器学习模型准确地逆转光学活性和非光学活性组件,改进时空分析.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 水资源管理 水资源管理
背景情况:
- 水库对于城市水,农业和生态系统至关重要.
- 卫星远程探测小型的,寡型/半型内陆水域面临着像分辨率,天气和营养变化等挑战,特别是对于非光学活性成分 (非OAC).
研究的目的:
- 开发和验证用于在水库中逆转光学活性元件 (OAC) 和非OAC的先进方法.
- 改进小型和中型内陆水体中水质变量的时空监测能力.
主要方法:
- 使用的无人机 (UAV),Sentinel-2B MSI和Landsat 7 ETM+表面反射率数据.
- 开发了一个模糊的C-means集群算法与带组合 (FCM-BC) 实证模型相结合.
- 采用机器学习模型,包括混合密度网络 (MDN),XGBoost,深度神经网络 (DNN) 和支持矢量回归 (SVR) 来逆转12个水质组件.
主要成果:
- 与非集群模型相比,FCM-BC模型显著提高了准确性 (平均R2增加至少46.9%).
- MDN模型表现出卓越的准确性 (R2 0.60-0.98) 和稳定性 (R2降低至13.2%).
- 无人机 (UAV) 数据在实证和机器学习方法中产生了更高的准确性;产生了一致的时空地图.
结论:
- 采用先进机器学习的无人机和卫星数据的综合方法有效监测水库水质,包括非OAC.
- 拟议的逆转策略增强了在各种条件下的监控能力.
- 结合无人机和卫星平台,可以改善小水体中OAC和非OAC的响应.
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