摘要
水质污染现象日趋严重,水质预测对于水质的保护尤为重要。然而水质受到多种水质指标的影响,并且水质监测数据是一种时序数据,各种指标间存在着复杂关系,现有的方法并不能充分捕捉水中各指标间的复杂关系。针对当前水质时序数据预测长距离依赖和多种指标间关系考虑不充分的问题,提出一种基于GAT-Transformer的水质预测方法。首先引入卷积神经网络(CNN)提取每个水质时间序列数据输入的高级特征;然后将CNN输出的数据输入GAT层和多头自注意力层,通过GAT捕捉水中各元素间的复杂关系;最后将GAT层和多头自注意力层的输出结合,通过Transformer进行预测输出。使用内蒙古某真实水质数据集进行实验,表明所提方法在MAE、MSE和RMSE三个指标上的综合表现优于其它方法。
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 indicators,and water quality monitoring data is a temporal data with complex relationships between various indicators.Existing methods cannot fully capture the complex relationships between various indicators in water.A water quality prediction method based on gat transformer is proposed to solve the problems of long-distance dependence of current water 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.
作者
王永生
陈振
刘利民
刘广文
WANG Yong-sheng;CHEN Zhen;LIN Li-min;LIU Guang-wen(College of Data Science and Application,Inner Mongolia University of Technology,Hohhot Inner Mongolia 010080,China;Inner Mongolia Autonomous Region Engineering&Technology Research Center of Big Data Based Software Service,Hohhot Inner Mongolia 010080,China)
出处
《计算机仿真》
2024年第3期321-326,358,共7页
Computer Simulation
基金
内蒙古科技应用计划项目(2020GG0094)
内蒙古自治区高等学校科学研究项目(NJZY21321)
内蒙古自治区自然科学基金项目(2021LHMS06001)
内蒙古自治区自然科学基金项目(2019MS03014)
内蒙古水利发展基金项目(NSK202109)
内蒙古自治区科技重大专项(2019ZD016)。
关键词
图注意力
水质预测
多头自注意力
Graph attention
Research on water quality prediction(SOWQP)
Multi-Head Attention
