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对于水质的深度学习
Wei Zhi1,2, Alison P Appling3, Heather E Golden4
1The National Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, Hohai University, Nanjing, China.
概括
深度学习为预测内陆水质提供了强大的解决方案,解决了气候极端和数据稀缺等挑战. 这种方法可以填补数据缺口,并确定关键的水质驱动因素.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 数据科学数据科学数据科学
背景情况:
- 由于气候极端和数据稀缺,预测内陆水质是复杂的.
- 传统模型与复杂的水质流程和数据限制作斗争.
研究的目的:
- 审查深度学习在水质科学中的潜力.
- 突出深度学习解决数据稀缺和识别水质驱动因素的能力.
主要方法:
- 对水质数据应用的深度学习方法的审查.
- 深度学习与传统的基于过程和统计模型的比较.
主要成果:
- 深度学习可以在高维水质数据中发现复杂的模式.
- 深度学习方法通过填补时间和空间的空白来有效地解决数据稀缺问题.
- 深度学习通过识别有影响力的水质驱动因素,有助于假设的制定和测试.
结论:
- 深度学习是一种有前途的,未被充分利用的方法,用于推进水质科学.
- 深度学习在预测水质和发现新知识方面比传统方法具有显著的优势.
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