Details

Autor(en) / Beteiligte

• Zhao, Ziqi
• Geng, Yuxin
• Ni, Qingjian
• Niu, Ben
• Shi, Yuhui
• Tan, Ying

Titel

An Improved Convolutional LSTM Network with Directional Convolution Layer for Prediction of Water Quality with Spatial Information

Ist Teil von

• Advances in Swarm Intelligence, p.94-105

Ort / Verlag

Cham: Springer International Publishing

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The prediction of water quality indicators is an important topic in environmental protection work. For the prediction of water quality data with multi-site data, this paper proposes an improved model based on ConvLSTM, which achieves the introduction of multi-site spatial relationships in water quality indicators prediction. On the basis of ConvLSTM, a directional convolutional layer is introduced to deal with the spatial dependence of multiple information collection stations with upstream and downstream relationship of a river to improve the prediction accuracy. The model proposed in this paper is applied to a dataset from three data collection stations to predict several indicators. Experiments on real-world data sets and results demonstrate that the improvements proposed in this paper make the model perform better compared to both the original and other common models.