Research on Water Quality Prediction Based on Gat-Transformer Time Series Model
The phenomenon of water pollution is becoming increasingly serious,and water quality prediction is particularly important for water quality protection.However,water quality is influenced by multiple water quality indi-cators,and water quality monitoring data is a temporal data with complex relationships between various indicators.Ex-isting methods cannot fully capture the complex relationships between various indicators in water.A water quality pre-diction method based on gat transformer is proposed to solve the problems of long-distance dependence of current wa-ter quality time series data prediction and insufficient consideration of the relationship between various indicators.First,convolutional neural network(CNN)was introduced to extract the advanced features of each water quality time series data input;Then the data output from CNN was input into the gat layer and the multi head attention layer,and capture the complex relationships among the elements in the water through the gat;Finally,the output of GAT layer and multi head attention layer were combined to predict the output through transformer.In this study,a real water quality data set in Inner Mongolia was used for experiments,which shows that the comprehensive performance of this method in Mae,MSE and RMSE,is better than other methods.
Graph attentionResearch on water quality prediction(SOWQP)Multi-Head Attention
NETL
NSTL
万方数据
