基于机器学习模型的河道水位预测

结合已有机器学习模型——卷积神经网络(CNN)和门控循环单元(GRU)的优点,构建了并联卷积循环神经网络(PCNN-GRU)模型,并将其用于赣江下游外洲站日尺度水位变化的预测。结果显示:相较于目前流行的长短时记忆(LSTM)模型、GRU模型以及卷积循环神经网络(CNN-GRU)模型,PCNN-GRU模型的均方根误差和平均绝对误差分别降低了18.39%、21.11%、15.48%和21.31%、18.64%、14.28%,纳什-萨特克里夫效率系数和准确率分别提高至0.999 2和88.12%,表明所建模型具有良好的预测性能,可用于河道水位预测。

Prediction of river water level based on machine learning model

Combining the advantages of the existing machine learning models, convolutional neural network (CNN) and gated recurrent unit (GRU), a parallel convolutional recurrent neural network (PCNN-GRU) model was constructed and was applied to the prediction of daily water level changes at the Waizhou station in the lower reaches of the Ganjiang River. The results show that, compared with long short-term memory (LSTM), GRU and convolutional recurrent neural network (CNN-GRU) models, the root mean square error and absolute average error of the PCNN-GRU model are decreased by 18.39%, 21.11%, 15.48% and 21.31%, 18.64%, 14.28%, respectively, and the Nash-Sutcliffe efficiency coefficient and accuracy rate are increased to 0.999 2 and 88.12%, respectively. This indicates that the PCNN-GRU model has good prediction performance, and can be used for river water level prediction.