Abstract：
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.
摘要：
深度学习模型为准确、稳定地预测河流水质提供了更为有力的方法,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，本研究提出了基于季节和趋势分解（STL）方法的两种集成模型TNX（具有时间关注）和STNX（具有时空关注），以使用地感时间序列数据预测水质。溶解氧,总磷,和氨氮在短步骤(1小时，和2小时)和长步长(12小时，和24h）在一条河流中设有七个水质监测点。集成模型TNX相对于用于短步和长步水质预测的最佳基线深度学习模型，性能提高了2.1％-6.1％和4.3％-22.0％，只需预测STL分解后原始数据的趋势分量，就能捕捉到水质参数的变化规律。STNX模型，有了时空注意力，与TNX模型相比，短步和长步水质预测的性能提高了0.5%-2.4%和2.3%-5.7%，这种改进更有效地减轻了长步预测的预测偏移模式。此外，模型解释结果一致地显示了所有监测点的正相关模式.然而,七个特定监测点的重要性随着预测监测点和输入监测点之间距离的增加而减弱。本研究为改善河流水质参数的短步和长步预测提供了一种基于STL分解的集成建模方法。了解复杂空间信息对深度学习模型的影响。
