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Debris flow prediction with machine learning: smart management of urban systems and infrastructures

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Abstract

Understanding the interaction of debris flow with different arrangements of buildings and providing an accurate and real-time predictions for the corresponding flow distribution has become a challenging task. Two parts of this problem, namely new constructions and rapid reaction, have made this as a daunting topic. Although the numerical methods can provide precise predictions, their applicability due to the above-mentioned issues is challenging as one has to redo the computations when new buildings are added. In this paper, thus, a deep learning model is developed to efficiently predict the spatiotemporal distribution of debris flow as it interacts with different building arrangements. As such, we employed U-net as the base model, which is combined with the residual network to alleviate the gradient vanishing issue. An autoregressive strategy is proposed to help the model capture the dynamics of the debris flow, where the output at the previous timestep is used as the input to predict the current timestep. A synthetic dataset based on the numerical methods is used to train the residual U-net model. The results indicate that the proposed method can predict the general trend at different timesteps, and its robustness is verified using an extensive sensitivity analysis.

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Acknowledgements

The financial support from the University of Wyoming for this research is greatly acknowledged.

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  1. College of Engineering and Applied Science, University of Wyoming, Laramie, WY, 82071, USA

    Tao Bai, Zhihao Jiang & Pejman Tahmasebi

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  1. Tao Bai
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  2. Zhihao Jiang
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  3. Pejman Tahmasebi
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Correspondence to Pejman Tahmasebi.

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Bai, T., Jiang, Z. & Tahmasebi, P. Debris flow prediction with machine learning: smart management of urban systems and infrastructures. Neural Comput & Applic 33, 15769–15779 (2021). https://doi.org/10.1007/s00521-021-06197-y

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