Information physics and quantum space technologies for natural hazard sensing, modelling and prediction

2021 ◽  
Author(s):  
Rui A. P. Perdigão
Keyword(s):  
Early Warning ◽  
Value Chain ◽  
Information Transfer ◽  
Dynamic Models ◽  
Early Warning Systems ◽  
Warning Signs ◽  
Added Value ◽  
Information Physics ◽  
Natural Transformations

Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.

scholarly journals Brief communication: Appropriate messaging is critical for effective earthquake early warning systems

2021 ◽  
Author(s):  
Meng Zhang ◽  
Xue Qiao ◽  
Barnabas C. Seyler ◽  
Baofeng Di ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Early Warning ◽  
Communication Channels ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
Warning Systems ◽  
The Public ◽  
Earthquake Early Warning Systems ◽  
Great Progress ◽  
And Training

Abstract. The earthquake early warning systems (EEWSs) in China have achieved great progress, with warning alerts being successfully delivered to the public in some regions. We examined the performance of the EEWS in China's Sichuan Province during the 2019 Changning Earthquake. Although its technical effectiveness was tested with the first alert released 10 s after the earthquake, we found that a big gap existed between the EEWS's message and the public's response. We highlight the importance of EEWS alert effectiveness and public participation for long-term resiliency, such as delivering useful alert messages through appropriate communication channels and training people to understand and properly respond.

scholarly journals Can Social Media Data Be Utilized to Enhance Early Warning: Retrospective Analysis of the U.S. Covid-19 Pandemic

2021 ◽  
Author(s):  
Lingyao Li ◽  
Lei Gao ◽  
Jiayan Zhou ◽  
Zihui Ma ◽  
David Choy ◽  
...  
Keyword(s):  
Social Media ◽  
Retrospective Analysis ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Signs ◽  
Warning Systems ◽  
Signal Ratio ◽  
Social Media Data ◽  
Media Data ◽  
The U.S

The U.S. needs early warning systems to help it contain the spread of infectious diseases. Conventional early warning systems use lab-test results or dynamic records to signal early warning signs. New early warning systems can supplement these data with indicators of public awareness like news articles and search queries. This study aims to explore the potential of utilizing social media data to enhance early warning of the COVID-19 outbreak. To demonstrate the feasibility, this study conducts a retrospective analysis and investigates more than 14 million related Twitter postings in the date range from January 20 to March 10, 2020. With the aid of natural language processing tools and machine learning classifiers, this study classifies each of these tweets into either a signal or a non-signal. In this study, a 'signal' tweet implies that the user recognized the COVID-19 outbreak risk in the U.S. This study then proposes a parameter 'signal ratio' to signal warning signs of the COVID-19 pandemic over periods. Results reveal that social media data and the signal ratio can detect the hazards ahead of the COVID-19 outbreak. This claim has been validated with a leading time of 16 days through the comparison to other referenced methods based on Google trends or media news.

scholarly journals Technology-Based Early Warning Systems for Bipolar Disorder: A Conceptual Framework

2016 ◽  
Vol 3 (3) ◽  
pp. e42 ◽  
Author(s):  
Colin Depp ◽  
John Torous ◽  
Wesley Thompson
Keyword(s):  
Bipolar Disorder ◽  
Conceptual Framework ◽  
Early Warning ◽  
Predictive Analytics ◽  
Early Warning Systems ◽  
Essential Elements ◽  
Warning Signs ◽  
Mobile Technologies ◽  
Warning Systems ◽  
Limited Information

Recognition and timely action around “warning signs” of illness exacerbation is central to the self-management of bipolar disorder. Due to its heterogeneity and fluctuating course, passive and active mobile technologies have been increasingly evaluated as adjunctive or standalone tools to predict and prevent risk of worsening of course in bipolar disorder. As predictive analytics approaches to big data from mobile health (mHealth) applications and ancillary sensors advance, it is likely that early warning systems will increasingly become available to patients. Such systems could reduce the amount of time spent experiencing symptoms and diminish the immense disability experienced by people with bipolar disorder. However, in addition to the challenges in validating such systems, we argue that early warning systems may not be without harms. Probabilistic warnings may be delivered to individuals who may not be able to interpret the warning, have limited information about what behaviors to change, or are unprepared to or cannot feasibly act due to time or logistic constraints. We propose five essential elements for early warning systems and provide a conceptual framework for designing, incorporating stakeholder input, and validating early warning systems for bipolar disorder with a focus on pragmatic considerations.

scholarly journals Time domain reflectometry (TDR) potential for soil condition monitoring of geotechnical assets

2019 ◽  
Vol 56 (7) ◽  
pp. 942-955 ◽  
Author(s):  
Giulio Curioni ◽  
David N. Chapman ◽  
Alexander C.D. Royal ◽  
Nicole Metje ◽  
Ben Dashwood ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Time Domain ◽  
Early Warning ◽  
Warning System ◽  
Early Warning Systems ◽  
Time Domain Reflectometry ◽  
Soil Conditions ◽  
Dry Density ◽  
Term Condition

The performance of geotechnical assets is influenced by various external factors including time and changing loading and environmental conditions. These changes could reduce the asset’s ability to maintain its function, potentially resulting in failure, which could be extremely disruptive and expensive to remediate; thus, the ability to monitor the long-term condition of the ground is clearly desirable as this could function as an early-warning system, permitting intervention prior to failure. This study demonstrates, for the first time, the potential of using time domain reflectometry (TDR) for long-term monitoring of the relative health of an asset (via water content and dry density) in a field trial where a clayey sandy silt was exposed to leaking water from a pipe. TDR sensors were able to provide detailed information on the variation in the soil conditions and detect abrupt changes that would relay a prompt for asset inspections or interventions. It is proposed that TDR could be used alone or together with other shallow geophysical techniques for long-term condition monitoring of critical geotechnical assets. Early-warning systems could be based on thresholds defined from the values or the relative change of the measured parameters.

scholarly journals Long-term Examination of Bank Crashes Using Panel Logistic Regression: Turkish Banks Failure Case

2016 ◽  
Vol 5 (3) ◽  
pp. 42 ◽  
Author(s):  
Birsen Eygi Erdogan
Keyword(s):  
Logistic Regression ◽  
Early Warning ◽  
Random Effect ◽  
Balance Sheet ◽  
Warning System ◽  
Early Warning Systems ◽  
Bank Failures ◽  
Return On Assets ◽  
Asset Quality

Crises in the financial sector over the last two decades have shown the importance of early warning systems, especially for bank failures. This study aims to develop an early warning system for Turkish commercial bank failures using panel data from 2002 to 2012. The data was analyzed using pooled logistic regression versus random panel logistic regression. The dependent variable was the bank failure, defined as the return-on assets ratio. Factor analysis was used to construct independent variables of financial ratios. The meaningful factors were found as: Interest income and expenditures, Equity, Other income and expenditures, Balance sheet, Deposit, Due, Asset quality. When the focus is sensitivity, the best prediction performance was obtained using random-effect logistic regression.

Precursory and post-failure analysis of landslide deformation in Danba County, China using optical remote sensing and Multi-temporal InSAR (MTI) methods with corner reflectors

2021 ◽  
Author(s):  
Zhuge Xia ◽  
Mahdi Motagh ◽  
Tao Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Early Warning ◽  
Early Warning Systems ◽  
Optical Remote Sensing ◽  
Study Results ◽  
Multi Temporal ◽  
Landslide Deformation ◽  
Landslide Kinematics

<p>On 17 June 2020, a large debris flow triggered by continuous heavy precipitation hit the Danba County in southwest China, blocked the river and a barrier lake was formed. Meanwhile, on the other side of the river, a large-scale landslide was triggered due to the reactivation of the ancient landslide body. Then an evacuation of more than 20000 people leaving their home town was urgently conducted.<br>This study exploits multi-sensor remote sensing techniques to assess landslide deformation, precursory deformation and post-failure motion of Danba landslide. We start with optical remote sensing images using the cross correlation method to investigate the overall information about this collapse, such as magnitude and moving direction of the sliding. Two high-resolution remote sensing optical images from Planet are processed right before and after the failure.<br>Moreover, we apply the advanced Multi-temporal InSAR (MTI) techniques such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subsets (SBAS) to analyze the precursors of the landslide over the long term. Based on the results of optical remote sensing, the descending Sentinel-1 data in 2014-2020 are extensively exploited with a better geometry of satellite observation. The long-term and transient of the deformation are analyzed against variations of precipitation, and then the related early warning systems are further explored.<br>The last stage of the work is the monitoring of current movements in the collapse region after the failure. It is explored by using multiple SAR datasets including C-band Sentinel-1 and X-band TerraSAR-X (TSX) high-resolution SAR images. With the help of the field works by our collaborators, stable artificial corner reflectors (CR) are deployed on selected sites to evaluate their performance in deriving landslide kinematics. Different from the traditional Triangle CR (TCR), the new design of dihedral CR (DCR) are introduced and exploited on the scene. The performance of this new design towards MTI processing and sub-pixel offset-tracking processing is examed and tested in this study. Results are presented and further discussed for a better assessment of Danba landslide.<br>The results of this paper can provide new strategies for developing an early warning system in this landslide using remote sensing technologies. Besides, the post-failure results are compared with the pre-event analysis, which could give an associated and comprehensive understanding of the whole landslide kinematics.</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 Implementation of Malaria Dynamic Models in Municipality Level Early Warning Systems in Colombia. Part I: Description of Study Sites

2014 ◽  
Vol 91 (1) ◽  
pp. 27-38 ◽  
Author(s):  
Daniel Ruiz ◽  
Gilma Mantilla ◽  
Salua Osorio ◽  
Stephen J. Connor ◽  
Madeleine C. Thomson ◽  
...  
Keyword(s):  
Early Warning ◽  
Dynamic Models ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Study Sites

scholarly journals Brief communication: Effective earthquake early warning systems: appropriate messaging and public awareness roles

2021 ◽  
Vol 21 (10) ◽  
pp. 3243-3250
Author(s):  
Meng Zhang ◽  
Xue Qiao ◽  
Barnabas C. Seyler ◽  
Baofeng Di ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Early Warning ◽  
Public Awareness ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
Warning Systems ◽  
The Public ◽  
Earthquake Early Warning Systems ◽  
Great Progress ◽  
And Training

Abstract. The earthquake early warning systems (EEWSs) in China have achieved great progress, with warning alerts being successfully delivered to the public in some regions. We examined the performance of the EEWS in China's Sichuan Province during the 2019 Changning earthquake. Although its technical effectiveness was tested with the first alert released 10 s after the earthquake, we found that a big gap existed between the EEWS's message and the public's response. We highlight the importance of EEWS alert effectiveness and public participation for long-term resiliency, such as delivering useful alert messages through appropriate communication channels and training people to understand and properly respond.

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