近年来水质预测成为水环境管理领域的热点问题，但是水环境本身的复杂性和动态性导致水质预测时预测精度低、模型稳定性差。针对这些问题，基于最优变分模态分解(Optimality Variational Mode Decomposition，OVMD)、时间卷积网络(Temporal Convolutional Network，TCN)、自回归模型(Autoregression，AR)提出了一种新的水质预测模型。首先，采用OVMD对原始数据进行分解，得到若干个子序列;然后，将分解的子序列作为TCN模型和AR模型的输入进行水质预测，并将两种模型的预测结果进行叠加重构得到最终预测结果;最后，采用龙华溪监测站的总磷数据进行实验验证。结果表明，OVMD-TCN-AR水质预测模型明显优于长短时记忆网络(Long Short Term Memory networks，LSTM)和长短期时间序列网络(Long-and Short-term Time-series network，LSTNet)，OVMD-TCN-AR水质预测模型的平均绝对误差为0。00660，均方根误差为0。01166，MAPE为0。0494，拟合度为0。97，说明OVMD-TCN-AR水质预测模型具有较高的可靠性和应用价值。
Water Quality Prediction Model based on OVMD-TCN-AR
In recent years,water quality prediction has become a hotspot in the field of water environment management.However,the complexity and dynamic nature of the water environment itself lead to low prediction accuracy and poor model stability during water quality prediction.To address these issues,a new water quality prediction model were proposed based on Optimality Variational Mode Decomposition(OVMD),Temporal Convolutional Network(TCN),and Autoregression(AR).First,OVMD was used to decompose the original data to obtain several sub-sequences.Then,the decomposed sub-sequences were used as inputs for TCN and AR models for water quality prediction,and the prediction results of the two models were stacked and reconstructed to obtain the final prediction result.Finally,the total phosphorus data from Longhua Creek monitoring station was used for experimental verification.The results showed that the OVMD-TCN-AR water quality prediction model significantly outperforms Long Short Term Memory networks(LSTM)and Long-and Short-term Time-series network(LSTNet).The average absolute error of the OVMD-TCN-AR water quality prediction model was 0.00660,the root mean square error was 0.01166,the MAPE was 0.0494,and the fitting degree was 0.97,indicating that the OVMD-TCN-AR water quality prediction model had high reliability and application value.
water qualitypredictionoptimal variational mode decompositiontime convolutional networkautoregressive model
万方数据
维普
