State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, China.
School of Big Data, Software Engineering, Chongqing University, Chongqing, 401331, China.
Environ Sci Pollut Res Int. 2020 May;27(14):16853-16864. doi: 10.1007/s11356-020-08087-7. Epub 2020 Mar 6.
As an important factor affecting the mangrove wetland ecosystem, water quality has become the focus of attention in recent years. Therefore, many studies have focused on the prediction of water quality to help establish a regulatory framework for the assessment and management of water pollution and ecosystem health. To make a more accurate and comprehensive forecast analysis of water quality, we propose a method for water quality prediction based on the multi-time scale bidirectional LSTM network. In the method, we improve data integrity and data volume through data preprocessing. And the network processes input data forward and backward and considers the dependencies at multiple time scales. Besides, we use the Box-Behnken experimental design method to adjust hyper-parameters in the process of modeling. In this study, we apply this method to the water quality prediction research of Beilun Estuary, and the performance of our proposed model is evaluated and compared with other models. The experiment results show that this model has better performance in water quality prediction than that of using LSTM or bidirectional LSTM alone. Graphical Abstract Schematic of research work.
作为影响红树林湿地生态系统的重要因素，水质近年来成为关注焦点。因此，许多研究都集中在水质预测上，以帮助建立水污染和生态系统健康评估和管理的监管框架。为了对水质进行更准确、全面的预测分析，我们提出了一种基于多时间尺度双向 LSTM 网络的水质预测方法。在该方法中，我们通过数据预处理来提高数据完整性和数据量。并且该网络对输入数据进行正向和反向处理，并考虑了多个时间尺度的相关性。此外，我们在建模过程中使用 Box-Behnken 实验设计方法来调整超参数。在本研究中，我们将该方法应用于北仑河口的水质预测研究，并对所提出模型的性能进行评估，并与其他模型进行比较。实验结果表明，该模型在水质预测方面的性能优于单独使用 LSTM 或双向 LSTM 的性能。
