The Institute of Municipal Engineering, Zhejiang University, Hangzhou, China.
Power China Huadong Engineering Corporation Limited, Hangzhou, China.
J Environ Manage. 2024 Aug;366:121932. doi: 10.1016/j.jenvman.2024.121932. Epub 2024 Jul 22.
Deep learning models provide a more powerful method for accurate and stable prediction of water quality in rivers, which is crucial for the intelligent management and control of the water environment. To increase the accuracy of predicting the water quality parameters and learn more about the impact of complex spatial information based on deep learning models, this study proposes two ensemble models TNX (with temporal attention) and STNX (with spatio-temporal attention) based on seasonal and trend decomposition (STL) method to predict water quality using geo-sensory time series data. Dissolved oxygen, total phosphorus, and ammonia nitrogen were predicted in short-step (1 h, and 2 h) and long-step (12 h, and 24 h) with seven water quality monitoring sites in a river. The ensemble model TNX improved the performance by 2.1%-6.1% and 4.3%-22.0% relative to the best baseline deep learning model for the short-step and long-step water quality prediction, and it can capture the variation pattern of water quality parameters by only predicting the trend component of raw data after STL decomposition. The STNX model, with spatio-temporal attention, obtained 0.5%-2.4% and 2.3%-5.7% higher performance compared to the TNX model for the short-step and long-step water quality prediction, and such improvement was more effective in mitigating the prediction shift patterns of long-step prediction. Moreover, the model interpretation results consistently demonstrated positive relationship patterns across all monitoring sites. However, the significance of seven specific monitoring sites diminished as the distance between the predicted and input monitoring sites increased. This study provides an ensemble modeling approach based on STL decomposition for improving short-step and long-step prediction of river water quality parameter, and understands the impact of complex spatial information on deep learning model.
深度学习模型为河流水质的准确和稳定预测提供了更强大的方法，这对于水环境的智能管理和控制至关重要。为了提高水质参数预测的准确性，并基于深度学习模型更多地了解复杂空间信息的影响，本研究提出了两种基于季节性和趋势分解（STL）方法的集成模型 TNX（具有时间注意力）和 STNX（具有时空注意力），用于使用地球感应时间序列数据预测水质。在一条河流的七个水质监测点，对溶解氧、总磷和氨氮进行了短步（1 小时和 2 小时）和长步（12 小时和 24 小时）预测。与最佳基线深度学习模型相比，集成模型 TNX 在短步和长步水质预测中的性能提高了 2.1%-6.1%和 4.3%-22.0%，并且通过仅预测原始数据的趋势分量，可以捕获水质参数的变化模式。具有时空注意力的 STNX 模型在短步和长步水质预测中，相对于 TNX 模型，分别获得了 0.5%-2.4%和 2.3%-5.7%的更高性能，并且这种改进在减轻长步预测的预测偏移模式方面更为有效。此外，模型解释结果一致表明，在所有监测站点之间存在正相关关系模式。然而，随着预测和输入监测站点之间的距离增加，七个特定监测站点的重要性降低。本研究提供了一种基于 STL 分解的集成建模方法，用于提高河流水质参数的短步和长步预测，并理解复杂空间信息对深度学习模型的影响。
