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A Novel Approach Combining Particle Swarm Optimization and Deep Learning for Flash Flood Detection from Satellite Images

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  • Do Ngoc Tuyen

    (School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 03000, Vietnam)

  • Tran Manh Tuan

    (Faculty of Information Technology, Thuyloi University, Hanoi 03000, Vietnam)

  • Le Hoang Son

    (VNU Information Technology Institute, Vietnam National University, Hanoi 03000, Vietnam)

  • Tran Thi Ngan

    (Faculty of Information Technology, Thuyloi University, Hanoi 03000, Vietnam)

  • Nguyen Long Giang

    (Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi 03000, Vietnam)

  • Pham Huy Thong

    (VNU Information Technology Institute, Vietnam National University, Hanoi 03000, Vietnam)

  • Vu Van Hieu

    (Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi 03000, Vietnam)

  • Vassilis C. Gerogiannis

    (Department of Digital Systems, Faculty of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece)

  • Dimitrios Tzimos

    (Department of Digital Systems, Faculty of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece)

  • Andreas Kanavos

    (Department of Digital Media and Communication, Ionian University, 28100 Kefalonia, Greece)

Abstract

Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellite images can be integrated with predictions based on sensors’ information to improve the accuracy of a forecasting system and subsequently trigger warning systems. The existing Deep Learning models such as UNET has been effectively used to segment the flash flood with high performance, but there are no ways to determine the most suitable model architecture with the proper number of layers showing the best performance in the task. In this paper, we propose a novel Deep Learning architecture, namely PSO-UNET, which combines Particle Swarm Optimization (PSO) with UNET to seek the best number of layers and the parameters of layers in the UNET based architecture; thereby improving the performance of flash flood segmentation from satellite images. Since the original UNET has a symmetrical architecture, the evolutionary computation is performed by paying attention to the contracting path and the expanding path is synchronized with the following layers in the contracting path. The UNET convolutional process is performed four times. Indeed, we consider each process as a block of the convolution having two convolutional layers in the original architecture. Training of inputs and hyper-parameters is performed by executing the PSO algorithm. In practice, the value of Dice Coefficient of our proposed model exceeds 79.75% (8.59% higher than that of the original UNET model). Experimental results on various satellite images prove the advantages and superiority of the PSO-UNET approach.

Suggested Citation

  • Do Ngoc Tuyen & Tran Manh Tuan & Le Hoang Son & Tran Thi Ngan & Nguyen Long Giang & Pham Huy Thong & Vu Van Hieu & Vassilis C. Gerogiannis & Dimitrios Tzimos & Andreas Kanavos, 2021. "A Novel Approach Combining Particle Swarm Optimization and Deep Learning for Flash Flood Detection from Satellite Images," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2846-:d:675788
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    References listed on IDEAS

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    1. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
    2. Purushottam Sharma & Kanak Saxena, 2017. "Application of fuzzy logic and genetic algorithm in heart disease risk level prediction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1109-1125, November.
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    Cited by:

    1. Kai Yu & Lujie Zhou & Pingping Liu & Jing Chen & Dejun Miao & Jiansheng Wang, 2022. "Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management," Mathematics, MDPI, vol. 10(21), pages 1-20, October.

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