scholarly journals Development of smart boulders to monitor mass movements via the Internet of Things: a pilot study in Nepal

2021 ◽  
Vol 9 (2) ◽  
pp. 295-315
Author(s):  
Benedetta Dini ◽  
Georgina L. Bennett ◽  
Aldina M. A. Franco ◽  
Michael R. Z. Whitworth ◽  
Kristen L. Cook ◽  
...  
Keyword(s):  
Debris Flow ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Area Network ◽  
Accelerometer Data ◽  
Warning Systems ◽  
2015 Gorkha Earthquake ◽  
Landslide Mass ◽  
Landslide Body

Abstract. Boulder movement can be observed not only in rockfall activity, but also in association with other landslide types such as rockslides, soil slides in colluvium originating from previous rockslides, and debris flows. Large boulders pose a direct threat to life and key infrastructure in terms of amplifying landslide and flood hazards as they move from the slopes to the river network. Despite the hazard they pose, boulders have not been directly targeted as a mean to detect landslide movement or used in dedicated early warning systems. We use an innovative monitoring system to observe boulder movement occurring in different geomorphological settings before reaching the river system. Our study focuses on an area in the upper Bhote Koshi catchment northeast of Kathmandu, where the Araniko highway is subjected to periodic landsliding and floods during the monsoons and was heavily affected by coseismic landslides during the 2015 Gorkha earthquake. In the area, damage by boulders to properties, roads, and other key infrastructure, such as hydropower plants, is observed every year. We embedded trackers in 23 boulders spread between a landslide body and two debris flow channels before the monsoon season of 2019. The trackers, equipped with accelerometers, can detect small angular changes in the orientation of boulders and large forces acting on them. The data can be transmitted in real time via a long-range wide-area network (LoRaWAN®) gateway to a server. Nine of the tagged boulders registered patterns in the accelerometer data compatible with downslope movements. Of these, six lying within the landslide body show small angular changes, indicating a reactivation during the rainfall period and a movement of the landslide mass. Three boulders located in a debris flow channel show sharp changes in orientation, likely corresponding to larger free movements and sudden rotations. This study highlights the fact that this innovative, cost-effective technology can be used to monitor boulders in hazard-prone sites by identifying the onset of potentially hazardous movement in real time and may thus establish the basis for early warning systems, particularly in developing countries where expensive hazard mitigation strategies may be unfeasible.

scholarly journals Development of smart boulders to monitor mass movements via the Internet of Things: A pilot study in Nepal

2020 ◽  
Author(s):  
Benedetta Dini ◽  
Georgina L. Bennett ◽  
Aldina M. A. Franco ◽  
Michael R. Z. Whitworth ◽  
Kristen L. Cook ◽  
...  
Keyword(s):  
Debris Flow ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Accelerometer Data ◽  
Warning Systems ◽  
2015 Gorkha Earthquake ◽  
Landslide Mass ◽  
Landslide Body ◽  
Rock Slides

Abstract. Boulder movement can be observed not only in rock fall activity, but also in association with other landslide types such as rock slides, soil slides in colluvium originated from previous rock slides and debris flows. Large boulders pose a direct threat to life and key infrastructure, amplifying landslide and flood hazards, as they move from the slopes to the river network. Despite the hazard they pose, boulders have not been directly targeted as a mean to detect landslide movement or used in dedicated early warning systems. We use an innovative monitoring system to observe boulder movement occurring in different geomorphological settings, before reaching the river system. Our study focuses on an area in the upper Bhote Koshi catchment northeast of Kathmandu, where the Araniko highway is subjected to periodic landsliding and floods during the monsoons and was heavily affected by coseismic landslides during the 2015 Gorkha earthquake. In the area, damage by boulders to properties, roads and other key infrastructure, such as hydropower plants, is observed every year. We embedded trackers in 23 boulders spread between a landslide body and two debris flow channels, before the monsoon season of 2019. The trackers, equipped with accelerometers, can detect small angular changes in boulders orientation and large forces acting on them. The data can be transmitted in real time, via a long-range wide area network (LoRaWAN®) gateway to a server. Nine of the tagged boulders registered patterns in the accelerometer data compatible with downslope movements. Of these, six lying within the landslide body show small angular changes, indicating a reactivation during the rainfall period and a movement consistent with the landslide mass. Three boulders, located in a debris flow channel, show sharp changes in orientation, likely corresponding to larger free movements and sudden rotations. This study highlights that this innovative, cost-effective technology can be used to monitor boulders in hazard prone sites, identifying in real time the onset of movement, and may thus set the basis for early warning systems, particularly in developing countries, where expensive hazard mitigation strategies may be unfeasible.

Monitoring boulder movement using the Internet of Things: towards a landslide early warning system

2020 ◽  
Author(s):  
Benedetta Dini ◽  
Georgina Bennett ◽  
Aldina Franco ◽  
Michael R. Z. Whitworth ◽  
Andreas Senn ◽  
...  
Keyword(s):  
Debris Flow ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Accelerometer Data ◽  
2015 Gorkha Earthquake ◽  
Landslide Body ◽  
Rock Slides

<p>Boulder movement can be observed not only in rock fall activity, but also in association with other landslide types such as rock slides, soil slides in colluvium originated from previous rock slides and debris flows.</p><p>Large boulders pose a direct threat to life and key infrastructure, causing destruction along their paths and amplifying landslide and flood hazards, as they move from the slopes to the river network. Despite the hazard they pose, boulders are generally not directly accounted for in hazard assessment methods, nor have they been targeted in dedicated early warning systems or used as a mean to detect landslide movement. The ability to monitor boulder movement in real time and to provide local stakeholders with timely alerts thus represents an important step forward.</p><p>Our study focuses on an area in the upper Bhote Koshi catchment northeast of Kathmandu, where the Araniko highway, a critical link between Nepal and China, is subjected to periodic landsliding and floods during the Monsoons and was heavily affected by coseismic landslides after the 2015 Gorkha earthquake. In the area, damage by boulders to properties, roads and other key infrastructure, such as hydropower plants, is observed every year.</p><p>In April 2019, we installed an innovative monitoring system to observe boulder movement occurring in different geomorphological settings on slopes, before reaching the river system. We embedded trackers in 23 boulders spread between a landslide body and two debris flow channels. The trackers are equipped with accelerometers and can detect, in real time, small angular changes in boulder positions as well as large forces acting on them. They are programmed to send regular data but, crucially, they can be triggered by movement and immediately transmit the data via a long-range wide area network gateway to a server.</p><p>Preliminary results show that 10 of the tagged boulders present patterns in the accelerometer data compatible with downslope movements. Of these, 6 lying within the landslide body show small angular changes, indicating a reactivation during the rainfall period and a movement consistent with the landslide mass. 4, located in a debris flow channel, show sharp changes in position, likely corresponding to larger free movements and rotations. The latter have not been found at their original location after the monsoon.</p><p>This study highlights that this innovative, cost-effective technology can be used to monitor boulders in prone sites and may set the basis for the development of an early warning system particularly in developing countries, where expensive mitigation strategies may be unfeasible.</p>

scholarly journals Application and verification of a multivariate real-time early warning method for rainfall-induced landslides: implication for evolution of landslide-generated debris flows

Landslides ◽  
2020 ◽  
Vol 17 (10) ◽  
pp. 2409-2419
Author(s):  
Zongji Yang ◽  
Liyong Wang ◽  
Jianping Qiao ◽  
Taro Uchimura ◽  
Lin Wang
Keyword(s):  
Debris Flow ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Mechanical Analysis ◽  
Warning Systems ◽  
Physical Mechanisms ◽  
New Strategy

Abstract Rainfall-induced landslides are a frequent and often catastrophic geological disaster, and the development of accurate early warning systems for such events is a primary challenge in the field of risk reduction. Understanding of the physical mechanisms of rainfall-induced landslides is key for early warning and prediction. In this study, a real-time multivariate early warning method based on hydro-mechanical analysis and a long-term sequence of real-time monitoring data was proposed and verified by applying the method to predict successive debris flow events that occurred in 2017 and 2018 in Yindongzi Gully, which is in Wenchuan earthquake region, China. Specifically, long-term sequence slope stability analysis of the in situ datasets for the landslide deposit as a benchmark was conducted, and a multivariate indicator early warning method that included the rainfall intensity-probability (I-P), saturation (Si), and inclination (Ir) was then proposed. The measurements and analysis in the two early warning scenarios not only verified the reliability and practicality of the multivariate early warning method but also revealed the evolution processes and mechanism of the landslide-generated debris flow in response to rainfall. Thus, these findings provide a new strategy and guideline for accurately producing early warnings of rainfall-induced landslides.

scholarly journals P-wave picking for earthquake early warning: Refinement of a T pd method

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Jim Mori
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Arrival Time ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Warning Systems ◽  
Wave Detection ◽  
Earthquake Early Warning Systems ◽  
Wave Arrival

Summary Detecting P-wave onsets for on-line processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection method designed for real-time processing of continuous waveforms. We used the new Tpd method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 s to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the Tpd method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The Tpd method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The Tpd method can be computed quickly so it would be suitable for the implementation in earthquake early warning systems.

scholarly journals Towards an operational database for real-time environmental monitoring and early warning systems

2017 ◽  
Vol 108 ◽  
pp. 2250-2259 ◽  
Author(s):  
Bartosz Balis ◽  
Marian Bubak ◽  
Daniel Harezlak ◽  
Piotr Nowakowski ◽  
Maciej Pawlik ◽  
...  
Keyword(s):  
Environmental Monitoring ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Monitoring And Early Warning

scholarly journals African Rainfall Climatology Version 2 for Famine Early Warning Systems

2013 ◽  
Vol 52 (3) ◽  
pp. 588-606 ◽  
Author(s):  
Nicholas S. Novella ◽  
Wassila M. Thiaw
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Correlation Coefficients ◽  
Global Precipitation Climatology Project ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Telecommunication System ◽  
African Rainfall ◽  
Global Telecommunication System ◽  
Rainfall Climatology

AbstractThis paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1° spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983–2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems.

scholarly journals Early Warning Systems for Currency Crises with Real-Time Data

2019 ◽  
Vol 30 (4) ◽  
pp. 813-835 ◽  
Author(s):  
Tjeerd M. Boonman ◽  
Jan P. A. M. Jacobs ◽  
Gerard H. Kuper ◽  
Alberto Romero
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Currency Crises ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Time Data ◽  
Real Time Data
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