A Low‐Cost<scp>IoT</scp>Framework for Landslide Prediction and Risk Communication

Chaturvedi, Pratik; Thakur, K. K.; Mali, Naresh; Uday, K. V.; Kumar, Sudhakar; Yadav, Srishti; Dutt, Varun

doi:10.1002/9781119456735.ch21

Cited by 11 publications

(7 citation statements)

References 20 publications

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“…During the past decade, there have been several developments on IoT-based systems for landslide prediction and early warning with different architectures and varying technologies in Device and Network layers. Some of these studies, e.g., Chaturvedi et al (2018), Khaing and Thein (2020)…”

Section: Development Of the Device Network And Platformmentioning

confidence: 99%

“…However, new, small, and less expensive types of wireless sensors for monitoring geotechnical parameters started to emerge in the market, being inspired by the enabling technology of the Internet of Things (IoT). Several research studies on adopting the IoT concept in landslide monitoring (Abraham et al 2020;Bhosale et al 2017;Chaturvedi et al 2018;Hou 2018) indicate that IoT can improve conventional monitoring with the provision of cost efficiency, flexibility, and ease to scale the system. Therefore, the hydrological monitoring system, in this study, was developed based on the state-of-the-art IoT technology employing public mobile networks.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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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“…Chaturbedi et al [3] designed an IoT based landslide monitoring framework which consisted of sensing units in which accelerometers measured ground accelerations and moistures sensor sensed soil moisture; data logging and thresholding unit and an alert generating unit. In this section, a detailed description of each of these components is provided.…”

Section: Comparative Analysis Of Key Features Of Iot Based Landslide Prediction Systemsmentioning

confidence: 99%

“…The Himalayan region, Eastern and Western Ghats, Vindhya range, Nilgiris, and Indo-Burmese range are some of the major landslide prone areas of India [2]. It is very important to identify the parameters which control the occurrence of landslides and develop an early warning system for minimizing these tremendous losses [3]. Protection of infrastructure and population is the major purpose of landslide monitoring.…”

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confidence: 99%

A Critical Assessment of IoT Based Landslide Prediction Systems Using AHP

Kar

Das

2021

Lecture Notes in Electrical Engineering

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Worldwide landslide is a serious hazard that causes great damage not only to the economic and societal development but also to precious human lives. Therefore, there is an urgent need for a good landslide prediction system. Recently, Internet of Things (IoT) has emerged as a popular technology that has a quick response to rapid changes in data. Hence, it has been widely used for landslide monitoring and prediction. Present study focuses on a review of existing IoT based landslide prediction systems. Ten most widely referred systems developed by various researchers have been reviewed. A comparative analysis of their key features has been performed. Prioritization of these ten systems has been done using Analytical Hierarchy Process (AHP) based on three factors, i.e., Cost, number of parameters sensed, and technology. The most suitable IoT based landslide prediction system as obtained from AHP based prioritization is recommended for implementation.Landslide is a geological phenomenon caused due to perceptible downward and outward movement of soil, rock, and vegetation under the influence of gravity. Landslides can be classified according to geotechnical properties of rocks, movement of materials, etc. Landslide movement can be of various types viz., fall, slide, topple, spread or flow. The velocity can vary from very slow to rapid [1]. Mountainous regions are generally considered landslide prone areas. In low-relief areas like roadway and relief excavations, landslides occur as cut and fill failures, river bluff failures, lateral spreading landslides, collapse of mine-waste piles (especially

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“…One of the major advancements introduced by innovative instrumentations is the possibility to overcome the so-called “manual philosophy”, thanks to the integration of Internet of things (IoT) technologies in the design process of automated monitoring systems [ 4 , 5 , 6 , 7 ]. When it comes to landslide monitoring, the integration of IoT-based devices results in a multilayered structure, where each component has a key role in guaranteeing the system functionality and efficiency [ 8 , 9 , 10 ]. While the architecture could vary from one case study to another, it is possible to identify some essential components for IoT-based monitoring systems: The perception layer, consisting of a network of connected sensors responsible for collecting the data and transmit them to the elaboration center; The data layer, which entails dataset storage and elaboration.…”

Section: Introductionmentioning

confidence: 99%

Advantages of IoT-Based Geotechnical Monitoring Systems Integrating Automatic Procedures for Data Acquisition and Elaboration

Carri¹,

Valletta

Cavalca

et al. 2021

Sensors

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Monitoring instrumentation plays a major role in the study of natural phenomena and analysis for risk prevention purposes, especially when facing the management of critical events. Within the geotechnical field, data collection has traditionally been performed with a manual approach characterized by time-expensive on-site investigations and monitoring devices activated by an operator. Due to these reasons, innovative instruments have been developed in recent years in order to provide a complete and more efficient system thanks to technological improvements. This paper aims to illustrate the advantages deriving from the application of a monitoring approach, named Internet of natural hazards, relying on the Internet of things principles applied to monitoring technologies. One of the main features of the system is the ability of automatic tools to acquire and elaborate data independently, which has led to the development of dedicated software and web-based visualization platforms for faster, more efficient and accessible data management. Additionally, automatic procedures play a key role in the implementation of early warning systems with a near-real-time approach, providing a valuable tool to the decision-makers and authorities responsible for emergency management. Moreover, the possibility of recording a large number of different parameters and physical quantities with high sampling frequency allows to perform meaningful statistical analyses and identify cause–effect relationships. A series of examples deriving from different case studies are reported in this paper in order to present the practical implications of the IoNH approach application to geotechnical monitoring.

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A Low‐CostIoTFramework for Landslide Prediction and Risk Communication

Cited by 11 publications

References 20 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

A Critical Assessment of IoT Based Landslide Prediction Systems Using AHP

Advantages of IoT-Based Geotechnical Monitoring Systems Integrating Automatic Procedures for Data Acquisition and Elaboration

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