Published in water 14(5), 2022
A novel Deep Learning model for runoff prediction based on Self Attention and LSTM.
Recommended citation: Chen, X., Huang, J., Wang, S., Zhou, G., Gao, H., Liu, M., ... & Qi, H. (2022). A new Rainfall-Runoff model using improved LSTM with attentive long and short Lag-Time. Water, 14(5), 697. https://doi.org/10.3390/w14050697
Published in Journal of Hydrology 615, 2022
Utilize Deep Learning models to simulate glacio-hydrological processes in the Urumqi Glacier No. 1 in northwest China.
Recommended citation: Chen, X., Wang, S., Gao, H., Huang, J., Shen, C., Li, Q., ... & Liu, M. (2022). Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation. Journal of Hydrology, 615, 128562.. https://doi.org/10.1016/j.jhydrol.2022.128562
Published in Engineering Applications of Artificial Intelligence Volume 121, , 2023
A novel Deep Learning model for time-series forecasting
Recommended citation: Wang, S., Chen, X., Ma, D., Wang, C., Wang, Y., Qi, H., ... & Liu, M. (2023). MIANet: Multi-level temporal information aggregation in mixed-periodicity time series forecasting tasks. Engineering Applications of Artificial Intelligence, 121, 106175. https://doi.org/10.1016/j.engappai.2023.106175
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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