College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, China E-mail:
College of Information, Shanghai Ocean University, Shanghai 201306, China.
Water Sci Technol. 2023 Nov;88(10):2611-2632. doi: 10.2166/wst.2023.357.
Accurate water quality predictions are critical for water resource protection, and dissolved oxygen (DO) reflects overall river water quality and ecosystem health. This study proposes a hybrid model based on the fusion of signal decomposition and deep learning for predicting river water quality. Initially, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to split the internal series of DO into numerous internal mode functions (IMFs). Subsequently, we employed multi-scale fuzzy entropy (MFE) to compute the entropy values for each IMF component. Time-varying filtered empirical mode decomposition (TVFEMD) is used to further extract features in high-frequency subsequences after linearly aggregating the high-frequency sequences. Finally, support vector machine (SVM) and long short-term memory (LSTM) neural networks are used to predict low- and high-frequency subsequences. Moreover, by comparing it with single models, models based on 'single layer decomposition-prediction-ensemble' and combination models using different methods, the feasibility of the proposed model in predicting water quality data for the Xinlian section of Fuhe River and the Chucha section of Ganjiang River was verified. As a result, the combined prediction approach developed in this work has improved generalizability and prediction accuracy, and it may be used to forecast water quality in complicated waters.
准确的水质预测对于水资源保护至关重要，而溶解氧（DO）反映了整体河流水质和生态系统健康状况。本研究提出了一种基于信号分解和深度学习融合的混合模型，用于预测河流水质。首先，采用完备集合经验模态分解自适应噪声（CEEMDAN）将 DO 的内部序列分解为多个固有模态函数（IMF）。然后，我们采用多尺度模糊熵（MFE）计算每个 IMF 分量的熵值。在线性聚合高频序列后，采用时变滤波经验模态分解（TVFEMD）进一步提取高频子序列中的特征。最后，使用支持向量机（SVM）和长短期记忆（LSTM）神经网络分别对低频和高频子序列进行预测。通过与单一模型进行比较，验证了基于“单层分解-预测-集成”的模型以及使用不同方法的组合模型在预测府河新联段和赣江滁槎段水质数据方面的可行性。结果表明，本研究提出的组合预测方法提高了泛化能力和预测精度，可用于预测复杂水域的水质。
