使用GEE和机器学习开发一种半自动化地表水质分析技术:Sundarbans的一个案例研究
Sheikh Fahim Faysal Sowrav1,2,3, Sujit Kumar Debsarma1, Mohan Kumar Das1,4
1National Oceanographic And Maritime Institute (NOAMI), Bangladesh.
Heliyon
|February 21, 2025
概括
这项研究引入了一种半自动化方法,用于Sundarbans水质监测. 机器学习模型准确地预测关键参数,为这个脆弱的生态系统提供了有效的替代方案.
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:
- 桑达尔班生态系统面临着气候变化和人类活动带来的重大威胁.
- 传统的水质监测方法往往耗费大量资源,对于大,偏远地区来说不切实际.
研究的目的:
- 开发和验证一个半自动化的方法来评估Sundarbans的水质.
- 使用机器学习集成现场和远程传感数据,以提高预测准确度.
- 提供一个可扩展和有效的工具,用于在生态敏感地区持续监测水质.
主要方法:
- 利用机器学习 (ML) 算法和实证贝叶斯基格 (EBK) 来预测水质参数 (SST,TSS,度,盐度,pH).
- 利用谷歌地球引擎 (GEE) 和AutoML与深度学习库进行动态模型创建.
- 集成的现场测量与远程传感数据用于模型校准和验证.
主要成果:
- 基于ML的模型在捕捉空间和时间水质变化方面表现出高准确性.
- 半自动化方法与现场测量有很强的一致性.
- 该EBK模型有效地插入了预测的水质参数.
结论:
- 开发的半自动化技术是Sundarbans持续和高效的水质监测的宝贵工具.
- 这种方法支持气候适应性和政策制定,通过早期检测水质恶化.
- 该研究推进了对脆弱生态系统的水质评估方法,促进了可持续的管理实践.
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