基于VMD和GA-BiLSTM组合模型的河流水质预测

River water quality prediction based on the combined model of VMD and GA-BiLSTM

  • 摘要: 溶解氧(DO)是河流水质监测的关键指标之一,为了精准预测河流水体中DO浓度,融合变分模态分解(VMD)、遗传算法(GA)和双向长短期记忆神经网络(BiLSTM),构建了VMD-GA-BiLSTM深度学习组合模型,对邢台市艾辛庄断面2020—2023年的DO浓度数据进行了训练与测试,并与多个经典的深度学习模型(BiLSTM、GA-BiLSTM、EMD-GA-BiLSTM)预测结果进行对比。结果表明:VMD-GA-BiLSTM模型在测试集上的均方根误差(RMSE)、平均绝对误差(MAE)、决定系数(R2)分别为0.149、0.135和0.974,相较于BiLSTM、GA-BiLSTM、EMD-GA-BiLSTM模型,RMSE分别降低0.464、0.307、0.290,MAE分别降低0.413、0.173、0.239,R2分别提升了0.216、0.133、0.088,表明构建的模型预测精度最高。将构建模型应用于邢台市后西吴桥断面对pH、DO和氨氮3项水质指标进行验证,与其他经典模型相比,VMD-GA-BiLSTM模型的RMSE、MAE最小且R²最大,可见其在水质时间序列数据预测方面具高度的通用性和稳定性。VMD-GA-BiLSTM模型能够准确预测DO浓度以及其他水质指标浓度,为水资源的可持续利用和水环境保护提供科学依据。

     

    Abstract: Dissolved oxygen (DO) is a pivotal indicator in river water quality monitoring. In order to accurately predict the DO concentration in river water bodies, we developed a VMD-GA-BiLSTM deep learning integration model, combining Variational Mode Decomposition (VMD), Genetic Algorithm (GA), and Bidirectional Long Short-term Memory (BiLSTM). We also conducted training and testing on DO concentration data from Aixinzhuang section in Xingtai City for the years 2020-2023, with comparisons made against the prediction results of multiple classic deep learning models (BiLSTM, GA-BiLSTM and EMD-GA-BiLSTM). The results revealed that the VMD-GA-BiLSTM model achieved remarkable performance on the test set, with RMSE of 0.149, MAE of 0.135, and R² of 0.974. When compared to BiLSTM, GA-BiLSTM and EMD-GA-BiLSTM models, the model demonstrated significant improvements: RMSE was reduced by 0.464, 0.307, and 0.290, respectively; MAE was decreased by 0.413, 0.173, and 0.239, respectively; and R² was increased by 0.216, 0.133, and 0.088, respectively. These findings demonstrated the superior prediction accuracy of the constructed model. To further validate the versatility and stability of the model, we applied it to predict three water quality indicators: pH, DO, and ammonia nitrogen, in Houxiwuqiao section of Xingtai City. The results indicated that the VMD-GA-BiLSTM model outperformed other classical models, achieving the lowest RMSE and MAE values and the highest R² score. This demonstrated the model's high adaptability and robustness in predicting water quality time series data. The research results showed that the VMD-GA-BiLSTM model can accurately predict concentrations of DO and other water quality indicators. This model can serve as a scientific basis for the sustainable utilization of water resources.

     

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