使用混合深度学习和常规水气天气数据预测河流每日最低溶解氧度
Yue Hu1, Chuankun Liu2, Wilfred M Wollheim3
1State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu 610059, China.
The Science of the total environment
|January 27, 2024
概括
预测河流溶解氧 (DO) 对水生生物的健康至关重要. 使用水文气象和水质数据的深度学习模型准确地预测每日最低DO,帮助流域管理.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 数据科学数据科学数据科学
背景情况:
- 溶解氧 (DO) 耗尽对水生生态系统构成重大威胁.
- 准确的DO预测对于有效的流域管理至关重要.
研究的目的:
- 为了预测河流中每日最低的DO度,使用易于获得的水文气象变量.
- 评估深度学习模型的性能,特别是LSTM和CNN-LSTM,用于DO预测.
主要方法:
- 利用了来自Oyster河流域的气象数据和高频水质测量 (CTD).
- 经过训练和验证的长期短期记忆 (LSTM) 和卷积神经网络-LSTM (CNN-LSTM) 模型.
- 从15分钟到24小时的聚合输入数据频率,以评估对模型性能的影响.
主要成果:
- CNN-LSTM模型表现出优越的预测稳定性,准确度与LSTM模型 (平均R2 = 0.839) 相比 (平均R2 = 0.865).
- 将输入数据频率聚合到24小时,显著提高了模型的准确性和稳定性.
- 包括预测的气象数据并没有提高模型性能.
结论:
- 深度学习模型提供了一种高效且低成本的方法来预测河流DO度.
- 优化数据聚合和模型选择是准确预测水质的关键.
- 这种方法通过可靠的水质预测来支持科学流域管理.
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