IoT-Based Strategies for Risk Management of Rainfall-Induced Landslides: A Review

Oguz, Emir Ahmet; Robinson, Kate; Depina, Ivan; Thakur, Vikas

doi:10.3850/978-981-11-2725-0-is13-2-cd

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…Due to practical issues related Fig. 2 The overall concept of the IoT-based hydrological monitoring of water-induced landslides and early warning systems (modified after Oguz et al 2019) to the selected subscriptions and national rollout plans for NB-IoT and LTE-M, the IoT Devices were configured to make use of the LTE-M part of the 4G network.…”

Section: Development Of the Device Network And Platformmentioning

confidence: 99%

IoT-based hydrological monitoring of water-induced landslides: a case study in central Norway

Oguz

Depina

Myhre

et al. 2022

Bull Eng Geol Environ

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Water-induced landslides pose a great risk to the society in Norway due to their high frequency and capacity to evolve in destructive debris flows. Hydrological monitoring is a widely employed method to understand the initiation mechanism of water-induced landslides under various climate conditions. Hydrological monitoring systems can provide relevant information that can be utilized in landslide early warning systems to mitigate the risk by issuing early warnings. These monitoring systems can be significantly enhanced, and wider deployments can be achieved through the recent developments within the domain of the Internet of Things (IoT). Therefore, this study aims to demonstrate a case study on an automated hydrological monitoring system supported by the IoT-based state-of-the-art technologies employing public mobile networks. Volumetric water content (VWC) sensors, suction sensors, and piezometers were used in the hydrological monitoring system to monitor the hydrological activities. The monitoring system was deployed in a case study area in central Norway at two locations of high susceptible geological units. During monitored period, the IoT-based hydrological monitoring system provided novel and valuable insights into the hydrological response of slopes to seasonally cold climates in terms of VWC and matric suction. The effects of rainfall, snow melting, ground freezing, and thawing were captured. The current study also made an attempt to integrate the collected data into a physical-based landslide susceptibility model to obtain a more consistent and reliable hazard assessment.

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Section: Development Of the Device Network And Platformmentioning

confidence: 99%

IoT-based hydrological monitoring of water-induced landslides: a case study in central Norway

Oguz

Depina

Myhre

et al. 2022

Bull Eng Geol Environ

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“…Its definition, "Sensors and actuators embedded in physical objects are linked through wired and wireless networks" [37], describes why the concept is well applicable to landslide monitoring and early warning. The IoT can help circumvent the problem of often very expensive monitoring systems and other possible problems such as difficult maintenance and rigidity of cable-based (non wireless) systems [38].…”

Section: The Internet Of Things and Landslide Monitoringmentioning

confidence: 99%

“…IoT systems consist of the Perception layer, the Network layer, the Middle-ware layer and the Application layer [38]. The Perception layer consists of multiple IoT devices that collect data, which is then sent via the Network layer to the Middle-ware layer.…”

Section: The Internet Of Things and Landslide Monitoringmentioning

confidence: 99%

Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems

Gamperl

Singer

Thuro

2021

Sensors

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Worldwide, cities with mountainous areas struggle with an increasing landslide risk as a consequence of global warming and population growth, especially in low-income informal settlements. Landslide Early Warning Systems (LEWS) are an effective measure to quickly reduce these risks until long-term risk mitigation measures can be realized. To date however, LEWS have only rarely been implemented in informal settlements due to their high costs and complex operation. Based on modern Internet of Things (IoT) technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication protocol, the Inform@Risk research project is developing a cost-effective geosensor network specifically designed for use in a LEWS for informal settlements. It is currently being implemented in an informal settlement in the outskirts of Medellin, Colombia for the first time. The system, whose hardware and firmware is open source and can be replicated freely, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g., tilt sensor) on board and can be connected to various different sensors including a newly developed low cost subsurface sensor probe for the detection of ground movements and groundwater level measurements. Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed LEWS offers a good benefit-cost ratio and in the future can hopefully find application in other parts of the world.

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“…The Internet of Things (IoT) deals with the physical world through a network of objects that are connected to the internet and process the collected information automatically [ 24 ]. Moreover, in recent years, the design of low-cost microcontroller boards based on IoT technologies has been spreading worldwide and has become increasingly important for the establishment of monitoring networks in landslide-prone areas [ 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Prototype of an IoT-Based Low-Cost Sensor Network for the Hydrological Monitoring of Landslide-Prone Areas

Marino

Quintero

Santonastaso

et al. 2023

Sensors

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Steep slopes covered by loose unsaturated pyroclastic deposits widely dispersed in Campania, Southern Italy, are often subjected to shallow landslides that turn into fast debris flows causing a large amount of damage and many casualties, triggered by heavy and persistent precipitation. The slope of Cervinara, located around 40 km northeast of Naples, was involved in a destructive flowslide between 15 and 16 December 1999, triggered by a rain event of 325 mm in 48 h. Hydrometeorological monitoring activities have been carried out near the landslide scarp of 1999 since 2017 to assess the water balance and to identify major hydrological processes involving the cover and the shallow groundwater system developing in the upper part of the underlying limestone fractured bedrock. Since 1 December 2022, a remotely accessible low-cost network has been installed to expand the field hydrological monitoring. The use of a network of low-cost capacitive sensors, communicating within the domain of Internet of Things (IoT) technology, aiming at dispersed monitoring of soil moisture, has been tested. Specifically, the tested prototype network allows measurements of the soil water content at two different points, communicating through a Wi-Fi-based IoT system using ESP32 boards. The ThingSpeakTM IoT platform has been used for remote field data visualization. Based on the obtained results, the prototype of this IoT-based low-cost network shows the potential to expand the amount of hydrological data, suitable for setting up early warning systems in landslide-prone areas.

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IoT-Based Strategies for Risk Management of Rainfall-Induced Landslides: A Review

Cited by 10 publications

References 4 publications

IoT-based hydrological monitoring of water-induced landslides: a case study in central Norway

IoT-based hydrological monitoring of water-induced landslides: a case study in central Norway

Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems

Prototype of an IoT-Based Low-Cost Sensor Network for the Hydrological Monitoring of Landslide-Prone Areas

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