Low-cost and Simple Early Warning Systems of Slope Instability

Author(s):  
Ikuo Towhata ◽  
Taro Uchimura
2021 ◽  
Author(s):  
Yi-Rong Yang ◽  
Tzu-Tung Lee ◽  
Tai-Tien Wang

Abstract Identifying cliffs that are prone to fall and providing a sufficient lead time for rockfall warning are crucial steps in disaster risk reduction and preventive maintenance work, especially that led by local governments. However, existing rockfall warning systems provide uncertain rockfall location forecasting and short warning times because the deformation and cracking of unstable slopes are not sufficiently detected by sensors before the rock collapses. Here, we introduce ground microtremor signals for early rockfall forecasting and demonstrate that microtremor characteristics can be used to detect unstable rock wedges on slopes, quantitatively describe the stability of slopes and lengthen the lead time for rockfall warning. We show that the change in the energy of ground microtremors can be an early precursor of rockfall and that the signal frequency decreases with slope instability. This finding indicates that ground microtremor signals are remarkably sensitive to slope stability. We conclude that microtremor characteristics can be used as an appropriate slope stability index for early rockfall warning systems and predicting the spatiotemporal characteristics of rockfall hazards. This early warning method has the advantages of providing a long lead time and on-demand monitoring, while increasing slope stability accessibility and prefailure location detectability.


2021 ◽  
Author(s):  
Moritz Gamperl ◽  
John Singer ◽  
Kurosch Thuro

<p>Worldwide, cities in mountainous areas struggle with increasing landslide risk as consequence of global warming and population growth, especially in low-income informal settlements. For these situations, current monitoring systems are often too expensive and too difficult to maintain. Therefore, innovative monitoring systems are needed in order to facilitate low-cost landslide early warning systems (LEWS) which can be applied easily.</p><p>Based on technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication standard, we are currently developing a cost-effective IoT (Internet of Things) geosensor network. It is specifically designed for local scale LEWS in informal settlements.</p><p>The system, which is open source and can be replicated without restrictions, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g. tilt sensor) on board and to which various additional sensors can be connected. The nodes are autonomous and can operate on standard batteries or solar panels. The sensor nodes can be installed on critical infrastructure such as house walls or foundations. Two of the possible additions are the Subsurface Sensor Node and the Low-Cost Inclinometer. Both are installed underground and offer tilt- and groundwater-measurements of the subsurface.</p><p>Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed monitoring system offers a great cost to benefit ratio and easy application for similar sites and LEWS, especially in urbanized areas in developing countries.</p><p>This work is being developed as part of the project Inform@Risk, where the monitoring system will be installed as part of an early warning system in Medellín, Colombia. It is funded by the German Ministry of Education and Research (BMBF).</p>


Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6183
Author(s):  
Nikolaos Peladarinos ◽  
Vasileios Cheimaras ◽  
Dimitrios Piromalis ◽  
Konstantinos G. Arvanitis ◽  
Panagiotis Papageorgas ◽  
...  

During the last two years, the COVID-19 pandemic continues to wreak havoc in many areas of the world, as the infection spreads through person-to-person contact. Transmission and prognosis, once infected, are potentially influenced by many factors, including indoor air pollution. Particulate Matter (PM) is a complex mixture of solid and/or liquid particles suspended in the air that can vary in size, shape, and composition and recent scientific work correlate this index with a considerable risk of COVID-19 infections. Early Warning Systems (EWS) and the Internet of Things (IoT) have given rise to the development of Low Power Wide Area Networks (LPWAN) based on sensors, which measure PM levels and monitor In-door Air pollution Quality (IAQ) in real-time. This article proposes an open-source platform architecture and presents the development of a Long Range (LoRa) based sensor network for IAQ and PM measurement. A few air quality sensors were tested, a network platform was implemented after simulating setup topologies, emphasizing feasible low-cost open platform architecture.


1995 ◽  
Vol 34 (05) ◽  
pp. 518-522 ◽  
Author(s):  
M. Bensadon ◽  
A. Strauss ◽  
R. Snacken

Abstract:Since the 1950s, national networks for the surveillance of influenza have been progressively implemented in several countries. New epidemiological arguments have triggered changes in order to increase the sensitivity of existent early warning systems and to strengthen the communications between European networks. The WHO project CARE Telematics, which collects clinical and virological data of nine national networks and sends useful information to public health administrations, is presented. From the results of the 1993-94 season, the benefits of the system are discussed. Though other telematics networks in this field already exist, it is the first time that virological data, absolutely essential for characterizing the type of an outbreak, are timely available by other countries. This argument will be decisive in case of occurrence of a new strain of virus (shift), such as the Spanish flu in 1918. Priorities are now to include other existing European surveillance networks.


10.1596/29269 ◽  
2018 ◽  
Author(s):  
Ademola Braimoh ◽  
Bernard Manyena ◽  
Grace Obuya ◽  
Francis Muraya

2005 ◽  
Author(s):  
Willian H. VAN DER Schalie ◽  
David E. Trader ◽  
Mark W. Widder ◽  
Tommy R. Shedd ◽  
Linda M. Brennan

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