Histogram of MAPE and RMSPE for each model under all factors of section A.

With all factor data, the figure demonstrates that W-WaveNet produces the best results or outcomes that are close to the best results.</p

Framework of W-WaveNet.

It combines the skip result of each spatio-temporal block with the stacked output, and finally compute the result through the LSTM network. All space-time blocks are connected to each other by skip connections.</p

The overall performance of each model in section A.

The overall performance of each model in section A.</p

The overall performance of each model in section B.

The overall performance of each model in section B.</p

Schematic diagram of the fusion model this model integrates WaveNet network, LSTM network and GCN network.

The data are first processed using convolution in the temporal dimension, followed by a spatial convolutional network in the spatial dimension, and finally reinforced by an LSTM network to correlate the front-to-back dependencies of the data.</p

The geographical position and site distribution of section A.

The geographical position and site distribution of section A.</p

MAE, RMSE and r2 for each model at pH and NH₃ in section A.

MAE, RMSE and r2 for each model at pH and NH3 in section A.</p

Data statistics of each water pollution factor in section A.

Data statistics of each water pollution factor in section A.</p

Training error and validation error curves of W-WaveNet model under all factors of section A.

Training error and validation error curves of W-WaveNet model under all factors of section A.</p

Time-series plots of NH₃ at section A predicted by each model.

This plot shows that the prediction curve of W-WaveNet is closer to the observed curve than other models.</p