根据时间序列分解和深度学习算法,对绿化湖度的长期趋势预测
Cheng Chen1, Mingtao Hu2, Qiuwen Chen3
1The National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China; Center for Eco-Environmental Research, Nanjing Hydraulic Research Institute, Nanjing 210029, China; College of Water Conservancy and Hydroelectric Power, Hohai University, Nanjing 210098, China.
The Science of the total environment
|August 12, 2024
概括
准确的长期预测甲基 (Chla) 对于湖泊管理至关重要. 混合深度学习模型通过分析水环境因素及其复杂的关系,有效地预测Chla趋势.
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:
- 长期预测叶绿素-a (Chla) 度对于管理湖泊的缩和污染至关重要,特别是随着气候变化.
- 由于趋势,季节性和残余成分以及非线性水环境因素关系,对Chla的时间序列分析是复杂的.
研究的目的:
- 开发和验证用于长期预测湖泊Chla趋势的混合方法.
- 为了研究Chla与太湖水环境因素之间的关系.
主要方法:
- 季节性和趋势分解使用Loess (STL) 进行时间序列组件分离.
- 波形连贯性用于识别时间频域关系和时间滞后.
- 卷积神经网络与双向长期短期记忆 (CNN-BiLSTM) 用于多变量预测.
主要成果:
- STL有效地将长期趋势与季节性和残余成分分开.
- 波束连贯性准确地确定了Chla和总 (TP) 和水温 (WT) 等因素之间的共振模式和时间滞后.
- 在预测准确度 (R2,RMSE,IOA) 和峰值捕获方面,CNN-BiLSTM模型显著超过了其他方法.
结论:
- 拟议的混合深度学习方法提供了一个有效的解决方案,用于长期Chla趋势预测在化的湖泊.
- 这种方法可以处理水环境数据的复杂属性,以改善藻类繁殖的预测和管理.
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