计算机科学
均方误差
卷积神经网络
水质
特征(语言学)
人工神经网络
人工智能
频道(广播)
水资源
对偶(语法数字)
特征提取
数据挖掘
机器学习
模式识别(心理学)
统计
数学
文学类
生态学
哲学
艺术
生物
语言学
计算机网络
作者
Yibei Liu,Peishun Liu,Xuefang Wang,Xueqing Zhang,Zifei Qin
出处
期刊:International Conference on Smart Transportation and City Engineering 2021
日期:2021-11-10
被引量:3
摘要
Water resources are the primary condition to maintain the ecological balance and sustainable development of the earth. Accurate prediction of water quality has high socio-economic value and ecological environmental protection value. However, it is difficult to achieve accurate prediction of river water quality data due to the characteristics of time series, seasonality, nonlinearity and excessive influencing factors. According to the characteristics of water quality data, this paper proposes a water quality prediction method based on attention mechanism of dual channel convolutional neural network ( CNN ) and long short-term memory ( LSTM ). Firstly, the river water quality data are cleaned and inputted into two parallel convolutional neural networks ( CNN ) for feature extraction. Then after the fusion level, the data are sent to the long short-term memory network ( LSTM ) for model training. Finally, the attention mechanism is used to optimize the model. The model combines powerful feature extraction ability of CNN , long-term memory ability of LSTM and the ability to highlight key features of attention mechanism ,achieving accurate prediction of river water quality data. Finally, based on the water quality data of the Guangli River, the results show that the Mean Absolute Error ( MAE ) of the proposed method is 2.04, and the Root Mean Square Guangli River Error ( RMSE ) is 2.77.
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