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Peer-reviewedCC BY 4.0PLOS One

Case studies on IoT monitoring of debris flow and landslides

  • Lei Huang1
  • Yung-Ming Cheng2,3
  • Shu-min Wei4
  • Miao-miao Gie5
5 affiliations
  1. 1College of Architecture and Civil Engineering, Sanming University, Sanming, China
  2. 2Department of Civil Engineering, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China
  3. 3Shenzhen Antai Data Monitoring Technology Company Limited, Shenzhen, China
  4. 4College of Architectural Engineering, Taishan College of Science and Technology, Taishan, China
  5. 5College of Civil Engineering and Architecture, Wenzhou University, Wenzhou, China
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Abstract

Aiming at the critical demand for real-time monitoring of slope disasters in mountainous and urbanized regions, this study develops Internet of Things (IoT)-based real-time monitoring systems to resolve the issues of data delay and fragmentation in conventional manual monitoring. Through three engineering cases conducted by the authors, we demonstrate the adaptability of IoT technology in slope disaster monitoring under different instability mechanisms (debris flows and landslides) and geological conditions (artificial-reinforced and semi-natural slopes). Specifically, in Case 1, a novel IoT monitoring system for debris flows integrated with the structural characteristics of rigid barriers is proposed. In Cases 2 and 3, multi-type monitoring sensors and equipment are deployed, including deformation sensors, rainfall gauges and video surveillance devices, to achieve accurate early warnings of rainfall-induced landslides. The novelty of this work lies in proposing a unified IoT architecture scheme applicable to different types of slope disasters. This scheme systematically integrates physico-mechanical sensing devices, a solar-battery collaborative power supply system, video monitoring equipment, data transmission methods, early warning modes, and a cloud-based processing module. Furthermore, the effectiveness of the proposed scheme is validated via successful early warning cases, and this scheme provides an effective technical framework for slope disaster prevention and control.

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Cite

BibTeXRIS
BibTeX
@article{huang2026case,
  title = {{Case studies on IoT monitoring of debris flow and landslides}},
  author = {Lei Huang and Yung-Ming Cheng and Shu-min Wei and Miao-miao Gie},
  journal = {PLOS One},
  year = {2026},
  month = nov,
  doi = {10.1371/journal.pone.0361584},
  url = {https://doi.org/10.1371/journal.pone.0361584},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Case studies on IoT monitoring of debris flow and landslides
AU  - Lei Huang
AU  - Yung-Ming Cheng
AU  - Shu-min Wei
AU  - Miao-miao Gie
PY  - 2026
DA  - 2026/11/04
JO  - PLOS One
DO  - 10.1371/journal.pone.0361584
UR  - https://doi.org/10.1371/journal.pone.0361584
AB  - Aiming at the critical demand for real-time monitoring of slope disasters in mountainous and urbanized regions, this study develops Internet of Things (IoT)-based real-time monitoring systems to resolve the issues of data delay and fragmentation in conventional manual monitoring. Through three engineering cases conducted by the authors, we demonstrate the adaptability of IoT technology in slope disaster monitoring under different instability mechanisms (debris flows and landslides) and geological conditions (artificial-reinforced and semi-natural slopes). Specifically, in Case 1, a novel IoT monitoring system for debris flows integrated with the structural characteristics of rigid barriers is proposed. In Cases 2 and 3, multi-type monitoring sensors and equipment are deployed, including deformation sensors, rainfall gauges and video surveillance devices, to achieve accurate early warnings of rainfall-induced landslides. The novelty of this work lies in proposing a unified IoT architecture scheme applicable to different types of slope disasters. This scheme systematically integrates physico-mechanical sensing devices, a solar-battery collaborative power supply system, video monitoring equipment, data transmission methods, early warning modes, and a cloud-based processing module. Furthermore, the effectiveness of the proposed scheme is validated via successful early warning cases, and this scheme provides an effective technical framework for slope disaster prevention and control.
SN  - 1932-6203
ER  -