Monitoring Slope Movement and Soil Hydrologic Behavior Using IoT and AI Technologies: A Systematic Review

Abstract

Landslides or slope failure pose a significant risk to human lives and infrastructures. The stability of slopes is controlled by various hydrological processes such as rainfall infiltration, soil water dynamics, and unsaturated soil behavior. Accordingly, soil hydrological monitoring and tracking the displacement of slopes become crucial to mitigate such risks by issuing early warnings to the respective authorities. In this context, there have been advancements in monitoring critical soil hydrological parameters and slope movement to ensure potential causative slope failure hazards are identified and mitigated before they escalate into disasters. With the advent of the Internet of Things (IoT), artificial intelligence, and high-speed internet, the potential to use such technologies for remotely monitoring soil hydrological parameters and slope movement is becoming increasingly important. This paper provides an overview of existing hydrological monitoring systems using IoT and AI technologies, including soil sampling, deploying on-site sensors such as capacitance, thermal dissipation, Time-Domain Reflectometers (TDRs), geophysical applications, etc. In addition, we review and compare the traditional slope movement detection systems, including topographic surveys for sophisticated applications such as terrestrial laser scanners, extensometers, tensiometers, inclinometers, GPS, synthetic aperture radar (SAR), LiDAR, and Unmanned Aerial Vehicles (UAVs). Finally, this interdisciplinary research from both Geotechnical Engineering and Computer Science perspectives provides a comprehensive state-of-the-art review of the different methodologies and solutions for monitoring landslides and slope failures, along with key challenges and prospects for potential future study.