Abstract
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.
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Zhao, Z., Geng, Y., Ni, Q. (2022). An Improved Convolutional LSTM Network with Directional Convolution Layer for Prediction of Water Quality with Spatial Information. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13345. Springer, Cham. https://doi.org/10.1007/978-3-031-09726-3_9
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