Risk Evaluation and Warning Threshold of Unstable Slope Using Tilting Sensor Array

Author(s):  
Wang Lin ◽  
Ichiro Seko ◽  
Makoto Fukuhara ◽  
Ikuo Towhata ◽  
Taro Uchimura ◽  
...  

Abstract Slope monitoring and early warning systems (EWS) are a promising approach toward mitigating landslide-induced disasters. Many large-scale sediment disasters result in the destruction of infrastructure and loss of human life. The mitigation of vulnerability to slope and landslide hazards will benefit significantly from early warning alerts. The authors have been developing monitoring technology that uses a Micro Electro Mechanical Systems (MEMS) tilt sensor array that detects the precursory movement of vulnerable slopes and informs the issuance of emergency caution and warning alerts. In this regard, the determination of alarm thresholds is very important. Although previous studies have investigated the recording of threshold values by an extensometer which installation of an extensometer at appropriate sites is also difficult. The authors prefer tilt sensors and have proposed a novel threshold for the tilt angle, which was validated in this study. This threshold has an interesting similarity to previously reported viscous models. Additionally, multi-point monitoring has recently emerged and allows for many sensors to be deployed at vulnerable slopes without disregarding the slope’s precursory local behaviour. With this new technology, the detailed spatial and temporal variation of the behaviour of vulnerable slopes can be determined as the displacement proceeds toward failure.

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2662
Author(s):  
Shifan Qiao ◽  
Chaobo Feng ◽  
Pengkun Yu ◽  
Junkun Tan ◽  
Taro Uchimura ◽  
...  

In recent decades, early warning systems to predict the occurrence of landslides using tilt sensors have been developed and employed in slope monitoring due to their low cost and simple installation. Although many studies have been carried out to validate the efficiency of these early warning systems, few studies have been carried out to investigate the tilting direction of tilt sensors at the slope surface, which have revealed controversial results in field monitoring. In this paper, the tilting direction and the pre-failure tilting behavior of slopes were studied by performing a series of model tests as well as two field tests. These tests were conducted under various testing conditions. Tilt sensors with different rod lengths were employed to investigate the mechanism of surface tilting. The test results show that the surface tilting measured by the tilt sensors with no rods and those with short rods located above the slip surface are consistent, while the tilting monitored by the tilt sensors with long rods implies an opposite rotational direction. These results are important references to understand the controversial surface tilting behavior in in situ landslide monitoring cases and imply the correlation between the depth of the slip surface of the slope and the surface tilting in in situ landslide monitoring cases, which can be used as the standard for tilt sensor installation in field monitoring.


2020 ◽  
Vol 2 ◽  
Author(s):  
Vera L. Trainer ◽  
Raphael M. Kudela ◽  
Matthew V. Hunter ◽  
Nicolaus G. Adams ◽  
Ryan M. McCabe

A heatwave that blanketed the northeast Pacific Ocean in 2013–2015 had severe impacts on the marine ecosystem through altered species composition and survival. A direct result of this marine heatwave was a sustained, record-setting harmful algal bloom (HAB), caused by the toxigenic diatom, Pseudo-nitzschia, that led to an unprecedented delay in harvest opportunity for commercial Dungeness crab (Metacarcinus magister) and closure of other recreational, commercial and tribal shellfish harvest, including razor clams. Samples collected during a cruise in summer 2015, showed the appearance of a highly toxic “hotspot” between Cape Mendocino, CA and Cape Blanco, OR that was observed again during cruises in the summers of 2016–2018. The transport of toxic cells from this retentive site northward during wind relaxations or reversals associated with storms resulted in economically debilitating delay or closure of Dungeness crab harvest in both northern California and Oregon in 2015–2019. Analyses of historic large-scale Pseudo-nitzschia HABs have shown that these events occur during warm periods such as El Niño, positive phases of the Pacific Decadal Oscillation, or the record-setting marine heatwave. In order to reduce the impacts of large-scale HABs along the west coast of North America, early warning systems have been developed to forewarn coastal managers. These early warning systems include the Pacific Northwest and California HAB Bulletins, both of which have documented elevated domoic acid and increased risk associated with the northern California hotspot. These early warnings enable mitigative actions such as selective opening of safe harvest zones, increased harvest limits during low risk periods, and early harvest in anticipation of impending HAB events. The aims of this study are to show trends in nearshore domoic acid along the US west coast in recent years, including the recent establishment of a new seed bed of highly-toxic Pseudo-nitzschia, and to explore how early warning systems are a useful tool to mitigate the human and environmental health and economic impacts associated with harmful algal blooms.


2020 ◽  
Author(s):  
Jamie Towner ◽  
Andrea Ficchí ◽  
Hannah L. Cloke ◽  
Juan Bazo ◽  
Erin Coughlan de Perez ◽  
...  

Abstract. Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive/negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979–2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative ENSO years when the SST anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing however does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO years compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods.


2021 ◽  
Vol 15 (02) ◽  
pp. 11-17
Author(s):  
Olivier Debauche ◽  
Meryem Elmoulat ◽  
Saïd Mahmoudi ◽  
Sidi Ahmed Mahmoudi ◽  
Adriano Guttadauria ◽  
...  

Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.


2021 ◽  
Author(s):  
Chiara Proietti ◽  
Alessandro Annunziato ◽  
Pamela Probst ◽  
Stefano Paris ◽  
Thomas Peter

<p>To improve preparedness and response in case of large-scale disasters, the international humanitarian community needs to understand the anticipated impact of an event as soon as possible in order to take informed operational decisions. The European Commission’s Joint Research Centre (JRC), DG ECHO, and the United Nations’ OCHA and UNOSAT launched the Global Disaster Alert and Coordination System (www.GDACS.org) in 2002-03 as cooperation platform to provide early disaster warning and coordination services to humanitarian actors. After more than 15 years, GDACS has around 30k registered users among humanitarian organisations at global level.</p><p>At the beginning, one of GDACS’s main tasks was the dissemination of automatic alerts for earthquakes, tsunamis and tropical cyclones; today, the system has been augmented to include also floods, droughts and volcanoes, and it will soon include forest fires.  Alerts are sent to the international humanitarian community to ensure timely warning in severe events that are expected to require international assistance. Alert levels are determined by automated algorithms without, or with very limited, human intervention, using automatic real-time data-feeds from various scientific institutes or the JRC’s own systems.</p><p>From 2020, because of the potential impact of the COVID-19 emergency on international preparedness and response activities, the COVID-19 situation in affected countries is now also monitored by the system, providing real time information updates on the website. This new feature allows to consider in the planning of the emergency response, the severity of the outbreak in the affected countries.</p><p>This contribution presents the challenges and outcomes of combining science-based information from different independent systems into a single Multi-Hazard Early Warning System and introduces new functionalities that were recently developed to address the new challenges related to the COVID-19 emergency.</p>


Author(s):  
Timothy B. Erickson ◽  
Noriko Endo ◽  
Claire Duvallet ◽  
Newsha Ghaeli ◽  
Kaitlyn Hess ◽  
...  

AbstractDuring the current global COVID-19 pandemic and opioid epidemic, wastewater-based epidemiology (WBE) has emerged as a powerful tool for monitoring public health trends by analysis of biomarkers including drugs, chemicals, and pathogens. Wastewater surveillance downstream at wastewater treatment plants provides large-scale population and regional-scale aggregation while upstream surveillance monitors locations at the neighborhood level with more precise geographic analysis. WBE can provide insights into dynamic drug consumption trends as well as environmental and toxicological contaminants. Applications of WBE include monitoring policy changes with cannabinoid legalization, tracking emerging illicit drugs, and early warning systems for potent fentanyl analogues along with the resurging wave of stimulants (e.g., methamphetamine, cocaine). Beyond drug consumption, WBE can also be used to monitor pharmaceuticals and their metabolites, including antidepressants and antipsychotics. In this manuscript, we describe the basic tenets and techniques of WBE, review its current application among drugs of abuse, and propose methods to scale and develop both monitoring and early warning systems with respect to measurement of illicit drugs and pharmaceuticals. We propose new frontiers in toxicological research with wastewater surveillance including assessment of medication assisted treatment of opioid use disorder (e.g., buprenorphine, methadone) in the context of other social burdens like COVID-19 disease.


2021 ◽  
Author(s):  
Paola Mazzoglio ◽  
Paolo Pasquali ◽  
Andrea Parodi ◽  
Antonio Parodi

<p>In the framework of LEXIS (Large-scale EXecution for Industry & Society) H2020 project, CIMA Research Foundation is running a 3 nested domain WRF (Weather Research and Forecasting) model with European coverage and weather radar data assimilation over Italy. Forecasts up to 48 hours characterized by a 7.5 km resolution are then processed by ITHACA ERDS (Extreme Rainfall Detection System), an early warning system for the heavy rainfall monitoring and forecasting. This type of information is currently managed by ERDS together with two global-scale datasets. The first one is provided by NASA/JAXA GPM (Global Precipitation Measurement) Mission through the IMERG (Integrated Multi-satellitE Retrievals for GPM) Early run data, a near real-time rainfall information with hourly updates, 0.1° spatial resolution and a 4 hours latency. The second one is instead provided by GFS (Global Forecast System) at a 0.25° spatial resolution.<br>The entire WRF-ERDS workflow has been tested and validated on the heavy rainfall event that affected the Sardinia region between 27 and 29 November 2020. This convective event significantly impacted the southern and eastern areas of the island, with a daily rainfall depth of 500.6 mm recorded at Oliena and 328.6 mm recorded at Bitti. During the 28th, the town of Bitti (Nuoro province) was hit by a severe flood event.<br>Near real-time information provided by GPM data allowed us to issue alerts starting from the late morning of the 28th. The first alert over Sardinia based on GFS data was provided in the late afternoon of the 27th, about 40 km far from Bitti. In the early morning of the 28th, a new and more precise alert was issued over Bitti. The first alert based on WRF data was instead provided in the morning of the 27th and the system continued to issue alerts until the evening of the 29th, confirming that, for this type of event, precise forecasts are needed to provide timely alerts.<br>Obtained results show how, taking advantage of HPC resources to perform finer weather forecast experiments, it is possible to significantly improve the capabilities of early warning systems. By using WRF data, ERDS was able to provide heavy rainfall alerts one day before than with the other data.<br>The integration within the LEXIS platform will help with the automatization by data-aware orchestration of our workflow together with easy control of data and workflow steps through a user-friendly web interface.</p>


2009 ◽  
Vol 9 (6) ◽  
pp. 1911-1919 ◽  
Author(s):  
G. Bellotti ◽  
M. Di Risio ◽  
P. De Girolamo

Abstract. This paper investigates the feasibility of Tsunami Early Warning Systems for small volcanic islands focusing on warning of waves generated by landslides at the coast of the island itself. The critical concern is if there is enough time to spread the alarm once the system has recognized that a tsunami has been generated. We use the results of a large scale physical model experiment in order to estimate the time that tsunamis take to travel around the island inundating the coast. We discuss how and where it is convenient to place instruments for the measurement of the waves.


Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 390
Author(s):  
Candice S. Charlton ◽  
Tannecia S. Stephenson ◽  
Michael A. Taylor ◽  
Christina A. Douglas

There is an increasing need to develop bushfire monitoring and early warning systems for Jamaica and the Caribbean. However, there are few studies that examine fire variability for the region. In this study the MODIS C6 Fire Archive for 2001–2019 is used to characterize bushfire frequencies across Jamaica and to relate the variability to large-scale climate. Using additive mixed model and backward linear regression, the MODIS represents 80% and 73% of the local Jamaica Fire Brigade (JFB) data variability for 2010–2015, respectively. However, the MODIS values are smaller by a factor of approximately 30. The MODIS climatology over Jamaica reveals a primary peak in March and a secondary maximum in July, coinciding with months of minimum rainfall. A significant positive linear trend is observed for July-August bushfire events over 2001–2019 and represents 29% of the season’s variability. Trends in all-island totals in other seasons or annually were not statistically significant. However, positive annual trends in Zone 2 (eastern Jamaica) are statistically significant and may support an indication that a drying trend is evolving over the east. Significant 5-year and 3.5-year periodicities are also evident for April–June and September–November variability, respectively. Southern Jamaica and particularly the parish of Clarendon, known for its climatological dryness, show the greatest fire frequencies. The study provides evidence of linkages between fire occurrences over Jamaica and oceanic and atmospheric variability over the Atlantic and Pacific. For example, all-island totals show relatively strong association with the Atlantic Multidecadal Oscillation. The study suggests that development of an early warning system for bushfire frequency that includes climate indices is possible and shows strong potential for fire predictions.


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