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Identification of Sequential Feature for Volcanic Ash Cloud Using FNN-LSTM Collaborative Computing

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2020)

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Abstract

Collaborative computing performs quickly and accurately the task via combining the multimedia, multi-methods, and multi-clients. Analyzing of traditional feedforward neural network (FNN), long short-term memory (LSTM) neural networks and remote sensing data, this paper proposes a new identification method of sequential feature based on FNN-LSTM collaborative calculation in the volcanic ash cloud monitoring. In this method, combining remote sensing data, the FNN network is used firstly to construct the identification model of volcanic ash cloud. Next, the LSTM network is used to identify the sequential feature of dynamic changes in volcanic ash cloud based on the text data of the volcanic ash report. And then the simulation and true volcanic ash cloud case is performed and analyzed. The experimental results show that: 1) the proposed method is high in training accuracy with 76.54% and testing accuracy with 77%, respectively, and has obvious advantages for small-scale data volumes; 2) the total accuracy and RMS of the simulation analysis reached 79.05% and 0.0149, respectively, it verified the feasibility and effectiveness in the prediction of spatiotemporal evolution; 3) the anti-noise property and the image segmentation effect is good, the obtained sequential feature of the volcanic ash cloud are closer to the actual diffusion. It can provide a reference for sequential feature extraction and dynamic monitoring of volcanic ash cloud in complex environments.

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Acknowledgements

This work was supported by the Science and Technology Development Foundation of Shanghai in China under Grant No. 19142201600 and Graduate Innovation and Entrepreneurship Program in Shanghai University in China under Grant No. 2019GY04.

Author information

Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Lan Liu & Xian-kun Sun

  2. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Cheng-fan Li

  3. Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai, 200444, China

    Cheng-fan Li

  4. Shanghai Shangda Hairun Information System Co Ltd., Shanghai, 200444, China

    Jiangang Shi

Authors
  1. Lan Liu
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  3. Xian-kun Sun
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  4. Jiangang Shi
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Corresponding author

Correspondence to Lan Liu .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. London South Bank University, London, UK

    Muddesar Iqbal

  4. Hangzhou Dianzi University, Hangzhou, China

    Yuyu Yin

  5. Zhejiang University, Hangzhou, China

    Jianwei Yin

  6. Fudan University, Shanghai, China

    Ning Gu

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Liu, L., Li, Cf., Sun, Xk., Shi, J. (2021). Identification of Sequential Feature for Volcanic Ash Cloud Using FNN-LSTM Collaborative Computing. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-67537-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-67537-0_17

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  • Print ISBN: 978-3-030-67536-3

  • Online ISBN: 978-3-030-67537-0

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