☆ 4.6 Review

Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review

SENSORS (2023)

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Summary: The increase in heavy rainfall events has led to an increase in floods and slope failures, posing severe risks to human lives. Monitoring and early warning systems are crucial in mitigating the damage caused by natural disasters. This study introduces a new monitoring system utilizing low-power wide-area networks (LPWANs), which successfully collects measurement data with the help of radio wave propagation tests. However, there are still errors in the data and an unstable correlation between temperature and inclination data.

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Summary: This paper summarizes the use of passive RFID tags for monitoring landslide surface displacement over the past five years. This method is effective for slow displacements under 1cm per day and offers high spatio-temporal resolution at a relatively low cost. The paper proposes data-processing solutions, including a complex-smoothing unwrapping algorithm, multi-frequency merging operation, and multi-tag and multi-antenna phase combining method, to overcome challenges encountered with long-term outdoor RFID localization. These approaches significantly improve data availability and phase unwrapping, making RFID-based earth surface displacement monitoring a robust and accurate solution.

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Summary: In this article, a smart monitoring wireless sensor networking (WSN) node device called SMARTMODE is introduced. SMARTMODE is a custom-designed device for monitoring landslide movement and causative parameters. It is energy-efficient, context-adaptive, reliable, and accurate. The device consumes low power in sleep mode and is capable of detecting minor slope variations. It also automatically adjusts data acquisition frequency based on environmental context, detects and filters faults in acquired data, and includes energy harvesting and backup approach for power outage issues. The performance of SMARTMODE was evaluated in an actual landslide location.

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Summary: This study explores the potential of integrating complex deep learning architectures into dynamic rockfall database population processes, aiming to relieve experts from manually classifying large amounts of change detection data. Utilizing deep neural networks based on PointNet and PointNet++ architectures, developed on a 5-year change detection database of over 8000 rock slope clusters for training, the models achieve high accuracy levels on the last two data acquisitions and a geologically different rockfall database.

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Summary: This paper presents a smart autonomous LoRa-based rockfall and landslide monitoring system, which operates in Pantelleria Island, Sicily, Italy. By utilizing wireless sensor networks and LoRa modulation technology, rockfalls and landslides can be remotely monitored.

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Summary: This study monitored the impact of construction and weather variables on rockfall activity in two rock slopes along a highway in western Colorado using TLS. Contrary to initial hypotheses, an increase in post-construction rockfall was recorded with a strong correlation to weather-related triggering factors.

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