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Landslide prediction based on low-cost and sustainable early warning systems with IoT

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Abstract

Under heavy rainfall, landslide early warning system (LEWS) is considered to be an effective method for providing timely warnings, but previous LEWS presents deficiencies such as high cost, high power consumption, and difficulties in secondary development. To address the shortcomings, the current study developed a low-cost and sustainable LEWS that integrates the Internet of Things (IoT) and an off-the-grid solar energy-powered integrated platform for data acquisition, data transmission, and data analysis. Obtained data such as soil moisture content, pore water pressure, deflection angle, and real-time factor of safety (Fs) are used as auxiliary warning indicators or cross-warning indicators. The LEWS considers three states before a landslide: monitoring state, alert state, and triggering state. Slope model tests and outdoor embankment slope tests were conducted to check the feasibility of the proposed LEWS. Results show that (1) compared with previous LEWS, the development cost and power consumption are greatly reduced, and the newly IoT-based LEWS provides an open architecture to meet different application scenarios and requirements and (2) a series of slope model tests based on LEWS successfully allows monitoring authorities to identify risk level, send warning signals, and predict potential movement so as to make enough time for risk management. The low-cost and standalone energy harvesting feature of the LEWS allows it to be applicable across the world.

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Data availability

All collected and processed data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

The Maeda Engineering Foundation, FY2022 Institute of Mathematics for Industry (IMI) Short-Term Joint Research Project ((2022a024 and 2022a025)), Cross-Ministerial Strategic Innovation Promotion Program (SIP) of Government of Japan and Kakenhi (21K04177). In addition, the first author is supported by Chinese Scholarship Council (No. 201907000123).

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Authors and Affiliations

  1. Graduate School of Engineering, Kyushu University, Fukuoka, Japan

    Yan Liu, Hemanta Hazarika & Divyesh Rohit

  2. School of Civil Engineering and Architecture, Wuyi university, Jiangmen, China

    Yan Liu

  3. Jiangmen City Transportation Bureau, Jiangmen, China

    Yan Liu

  4. Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

    Haruichi Kanaya

  5. ALSENS Inc., Sagamihara, Japan

    Osamu Takiguchi

Authors
  1. Yan Liu
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  2. Hemanta Hazarika
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  3. Haruichi Kanaya
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  4. Osamu Takiguchi
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  5. Divyesh Rohit
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Correspondence to Yan Liu.

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Liu, Y., Hazarika, H., Kanaya, H. et al. Landslide prediction based on low-cost and sustainable early warning systems with IoT. Bull Eng Geol Environ 82, 177 (2023). https://doi.org/10.1007/s10064-023-03137-z

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  • DOI: https://doi.org/10.1007/s10064-023-03137-z

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