Research on the Temporal and Spatial Prediction of the Water Quality of Beijing-Hangzhou Canal Based on Graph Convolution STG-LSTM

Rapid and accurate prediction of river water quality is an important task of urban water management strategy. However,River water quality factors have the characteristics of time series,instability,and nonlinearity,and are affected by many factors,which will cause differences in spatial and temporal dimensions. Most of the existing water quality factor forecasting methods are time series forecasting at a single monitoring station,which cannot describe the spatial distribution of river water quality factors. In this paper,we proposes a spatiotemporal prediction model of river water quality（STG-LSTM）on account of spatio-temporal graph convolution and long-short-term memory neural network. Based on the historical observation values of the geographic location and water quality factors of each monitoring station,construct a time-space map to characterize the time-space correlation between each monitoring station. Input the space-time diagram into the STG-LSTM model,using graph convolution（GCN）to obtain the spatial dependence of river water quality factor data and fusing the long and short-term memory neural network（LSTM）to obtain the spatio-temporal correlation of the water quality factor data,we realized the future period temporal and spaltial prediction of water quality at different locations in the canal section. The data sets of four different water quality factors at eight monitoring stations on the Changzhou section of the Beijing-Hangzhou Canal were used for verification. The model was compared with six other prediction models in terms of prediction accuracy and training time,and the reliability of the model was tested. The experimental results show that STG-LSTM can obtain high prediction accuracy with a short training time and realize rapid and accurate prediction of water quality at different locations of the river. Most but not the least,they provide technical support for urban water management.