环境科学
水质
采样(信号处理)
水文气象
水文学(农业)
生态学
计算机科学
气象学
地理
工程类
生物
计算机视觉
滤波器(信号处理)
降水
岩土工程
作者
Xiaoyu Wang,Xiaoyi Tang,Mei Zhu,Zhennan Liu,Guoqing Wang
出处
期刊:Water Research
[Elsevier BV]
日期:2024-07-03
卷期号:261: 122027-122027
被引量:14
标识
DOI:10.1016/j.watres.2024.122027
摘要
Depletion of dissolved oxygen (DO) is a significant incentive for biological catastrophic events in freshwater lakes. Although predicting the DO concentrations in lakes with high-frequency real-time data to prevent hypoxic events is effective, few related experimental studies were made. In this study, a short-term predicting model was developed for DO concentrations in three problematic areas in China's Chaohu Lake. To predict the DO concentrations at these representative sites, which coincide with biological abnormal death areas, water quality indicators at the three sampling sites and hydrometeorological features were adopted as input variables. The monitoring data were collected every 4 h between 2020 and 2023 and applied separately to train and test the model at a ratio of 8:2. A new AC-BiLSTM coupling model of the convolution neural network (CNN) and the bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) was proposed to tackle characteristics of discontinuous dynamic change of DO concentrations in long time series. Compared with the BiLSTM and CNN-BiLSTM models, the AC-BiLSTM showed better performance in the evaluation criteria of MSE, MAE, and R
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