The 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.
Center for Eco-Environmental Research, Nanjing Hydraulic Research Institute, Nanjing 210029, China.
Sci Total Environ. 2024 Nov 15;951:175451. doi: 10.1016/j.scitotenv.2024.175451. Epub 2024 Aug 10.
Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task due to the mixture of trend, seasonal and residual components in time series and the nonlinear relationships between Chla and the hydro-environmental factors. Here we developed a hybrid approach for long-term trend forecast of Chla in lakes, taking the Lake Taihu as an instantiation case, by the integration of Seasonal and Trend decomposition using Loess (STL), wavelet coherence, and Convolutional Neural Network with Bidirectional Long Short-Term Memory (CNN-BiLSTM). The results showed that long-term trends of Chla and the hydro-environmental factors could be effectively separated from the seasonal and residual terms by STL method, thereby enhancing the characterization of long-term variation. The resonance pattern and time lag between Chla and the hydro-environmental factors in the time-frequency domain were accurately identified by wavelet coherence. Chla responded quickly to variations in TP, but showed a time lag response to variations in WT in Lake Taihu. The forecasting method using multivariate and CNN-BiLSTM largely outperformed the other methods for Lake Taihu with regards to R, RMSE, IOA and peak capture capability, owning to the combination of CNN for extracting local features and the integration of bidirectional propagation mechanism for the acquisition of higher-level features. The proposed hybrid deep learning approach offers an effective solution for the long-term trend forecast of algal blooms in eutrophic lakes and is capable of addressing the complex attributes of hydro-environmental data.
叶绿素-a 浓度（Chla）的长期趋势预测对湖泊富营养化管理和污染控制规划具有重要意义，特别是在气候变化背景下。然而，由于时间序列中趋势、季节性和剩余分量的混合以及 Chla 与水环境保护因素之间的非线性关系，这是一项具有挑战性的任务。在这里，我们通过季节性和趋势分解使用 Loess（STL）、小波相干性以及卷积神经网络与双向长短期记忆（CNN-BiLSTM）的集成，以太湖为例，开发了一种湖泊 Chla 长期趋势预测的混合方法。结果表明，STL 方法可以有效地将 Chla 和水环境保护因素的长期趋势与其季节性和剩余项分离，从而增强了长期变化的特征描述。小波相干性准确地识别了 Chla 与水环境保护因素在时频域中的共振模式和时滞。Chla 对 TP 的变化反应迅速，但在太湖中对 WT 的变化表现出时间滞后响应。与其他方法相比，使用多元和 CNN-BiLSTM 的预测方法在太湖的 R、RMSE、IOA 和峰值捕获能力方面表现出色，这归功于 CNN 用于提取局部特征的组合以及双向传播机制的集成，以获取更高层次的特征。该混合深度学习方法为富营养化湖泊的藻类爆发长期趋势预测提供了有效的解决方案，并能够解决水环境保护数据的复杂属性。
