Abstract Turbidity is one of the important indicators in water quality management of reservoir. There are many factors affecting turbidity, and its time series is non-linear, making prediction difficult. Therefore, it is necessary to carry out research on reservoir turbidity prediction methods. In this study, the Long Short-Term Memory (LSTM) neural network was identified, validated and tested for the computation of turbidity in the Qingcaosha Reservoir. The model employed historical data of turbidity, water level, wind direction and wind speed over a period of 2 years at various monitoring points. Within 40 iterations of the model, the mean square error converged to less than 0.05 steadily, and the Nash efficiency coefficient of the 24 h prediction was above 0.5. It showed that the model has the characteristics of fast convergence, high stability, and accurate prediction, which meant this model can be well applied to prediction of reservoir turbidity. This study also tried to use the forecasted wind field data to improve the actual turbidity prediction of the reservoir. The results showed that the accuracy is slightly lower than the predicted result using the measured wind field data, but it was significantly higher than the prediction result using the extended wind field data at the previous time point. Therefore, using forecasted wind field data can effectively improve the accuracy of the actual reservoir turbidity forecast. The results of this study indicate that the LSTM neural network model is fast, stable, and highly accurate, indicating that it is suitable for prediction of turbidity in reservoirs and can provide support for water quality management of reservoirs.

中文翻译：

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基于长短期记忆神经网络的储层浊度预测方法研究

摘要 浊度是水库水质管理的重要指标之一。影响浊度的因素很多，其时间序列是非线性的，预测困难。因此，有必要开展储层浊度预测方法的研究。在这项研究中，长短期记忆 (LSTM) 神经网络被识别、验证和测试用于计算青草沙水库的浊度。该模型采用了各监测点2年的浊度、水位、风向和风速的历史数据。在模型的40次迭代中，均方误差稳定收敛到小于0.05，24 h预测的纳什效率系数在0.5以上。表明该模型具有收敛快、稳定性高、且预测准确，这意味着该模型可以很好地应用于储层浊度的预测。本研究还尝试利用预测的风场数据来改进水库的实际浊度预测。结果表明，精度略低于使用实测风场数据的预测结果，但明显高于使用前一个时间点扩展风场数据的预测结果。因此，利用预报的风场数据可以有效提高实际水库浊度预报的准确性。本研究结果表明，LSTM神经网络模型快速、稳定、准确度高，适用于水库浊度预测，可为水库水质管理提供支持。