scholarly journals Electromagnetic contribution to the resilience improvement against the Vrancea intermediate depth earthquakes, Romania

2020 ◽  
Vol 63 (5) ◽  
Author(s):  
Dragoș Armand Stănică ◽  
Dumitru Stănică ◽  
Monica Valeca ◽  
Ștefan Iordache
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Warning System ◽  
Random Variable ◽  
Anomalous Behavior ◽  
Time Intervals ◽  
Intermediate Depth ◽  
Daily Data ◽  
Variable Equation ◽  
Band Pass

In this paper we used the geomagnetic data, collected in real time on the intervals August- September and November-December, 2016, to emphasize possible relationships between the anomalous behavior of the normalized function Bzn and the both M5.7 and M5.6 earthquakes, generated at 72 km and, respectively 71 km depth, in the seismic active Vrancea zone on September 24 and December 28, 2016. Daily mean distributions of the Bzn and its standard deviation (SD) are obtained for the both time-intervals, in the ULF frequency range 0.001Hz - 0.0083Hz, by using the FFT band-pass filtering. We investigate the singularities of the pre-seismic anomalous signals related to the M5.7 and M5.6 earthquakes applying a statistical analysis based on a standardized random variable equation, and the results are presented as Bz* time series performed on the new time intervals 1-30 September and 1-31 December, 2016. Finally, two pre-seismic anomalous signals are observed: first one on September 21, with values greater than 5 SD, what means a lead time of 3 days before the onset of M5.7 earthquake; the second one, with values larger than 4 SD, which was identified on December 21 with 7 days prior to M5.6earhquake. In conclusion, as the work-station has specific programs for data processing, analyses and real time (daily) data display on the institute website, it may be used as an early warning system able to provide useful information for resilience improvement against the Vrancea intermediate depth seismicity.

scholarly journals Possible Correlations between the ULF Geomagnetic Signature and Mw6.4 Coastal Earthquake, Albania, on 26 November 2019

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 233
Author(s):  
Dragoș Armand Stănică ◽  
Dumitru Stănică
Keyword(s):  
Time Series ◽  
Lead Time ◽  
Low Frequency ◽  
Random Variable ◽  
Strain Effect ◽  
Signal Identification ◽  
Variable Equation ◽  
Band Pass ◽  
Time Invariant ◽  
Local Anomalies

An earthquake of Mw6.4 hit the coastal zone of Albania on 26 November 2019, at 02:54:11 UTC. It was intensively felt at about 34 km away, in Tirana City, where damages and lives lost occurred. To emphasize a pre-seismic geomagnetic signature before the onset of this earthquake, the data collected on the interval 15 October–30 November 2019, at the Panagjurishte (PAG)-Bulgaria and Surlari (SUA)-Romania observatories were analyzed. Further on, for geomagnetic signal identification we used the polarization parameter (BPOL) which is time invariant in non-seismic conditions and it becomes unstable due to the strain effect related to the Mw6.4earthquake. Consequently, BPOL time series and its standard deviations are performed for the both sites using ultra low frequency (ULF)-fast Fourier transform (FFT) band-pass filtering. A statistical analysis, based on a standardized random variable equation, was applied to emphasize on the BPOL* (PAG) and ABS BPOL* (PAG) time series the anomalous signal’s singularity and, to differentiate the transient local anomalies due to the Mw6.4 earthquake, from the internal and external parts of the geomagnetic field, taken PAG observatory as reference. Finally, the ABS BPOL* (PAG-SUA) time series were obtained on the interval 1–30 November 2019, where a geomagnetic signature greater than 2.0, was detected on 23 November and the lead time was 3 days before the onset of the Mw6.4earthquake.

scholarly journals Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1571 ◽  
Author(s):  
Song ◽  
Park ◽  
Lee ◽  
Park ◽  
Song
Keyword(s):  
Decision Making ◽  
Water Level ◽  
Real Time ◽  
Lead Time ◽  
Flash Flood ◽  
Warning System ◽  
Flood Forecasting ◽  
Urban Stream ◽  
Forecasting And Warning ◽  
Flood Warnings

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

Possible correlation beteen the pre-seismic geomagnetic signal and the M6.4 earthquake generated in the coastal zone of Albania, on November 26, 2019

2020 ◽  
Author(s):  
Dumitru Stanica ◽  
Dragos Armand Stanica
Keyword(s):  
Coastal Zone ◽  
Random Variable ◽  
Tectonic Stress ◽  
Strain Effect ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Anomalous Signal ◽  
Variable Equation ◽  
Band Pass ◽  
Time Invariant

<p>A catastrophic earthquake of magnitude Mw6.4 generated at 10km depth hit coastal zone of Albania on November 26-th 2019, at 2h54min UTC. The earthquake was intensively felt at about 34km far, in Tirana City, where a lot of damages have occurred. Consequently, in order to identify the anomalous geomagnetic signature before the onset of this earthquake, we retrospectively analyzed the data collected on the interval October 15–November 30, 2019 at the two geomagnetic observatories: Panagjurishte (PAG)-Bulgaria and Surlari (SUA)-Romania, the last one taken as reference. The pre-seismic geomagnetic anomalous signal is postulated to be due to the electrical conductivity changes, most probably associated with the earthquake-induced tectonic stress, followed by rupture and electrochemical processes deployed along the Adria plate subduction zone. To identify a pre-seismic geomagnetic signal related to this earthquake we used: (i) polarization parameter BPOL which should be time invariant in non-seismic condition and it becomes unstable before the onset a seismic event; (ii) Strain effect-related to the anomalous geomagnetic signals identification. Thus, the daily mean distributions of the BPOL and its standard deviations (SD) are performed for the both observation site (PAG and SUA) by using the FFT band-pass filter analysis in the ULF range (0.001Hz - 0.0083Hz). Further on, a statistical analysis based on a standardized random variable equation was applied for the two particular cases: a) the assessment of the singularity for anomalous signal, related to the Mw6.4earthquake, observed on the daily mean distributions of the BPOL*(PAG) and BPOL*(SUA); b) the differentiation of the transient local anomalies associated with Mw6.4earthquake from the internal and external parts of the geomagnetic field, taking Geomagnetic Observatory (SUA) as reference, and the result is presented as daily mean distribution of the BPOL*(PAG-SUA). Finally, on the BPOL*(PAG-SUA) time series, carried out on the interval 1-30 November 2019, a very clear anomaly of maximum greater than 2.5 SD was detected on November 22, what means a lead time of 4 days before the onset of Mw6.4earthquake. In consequence, all mentioned results could offer opportunities to develop new tools for early detection of geomagnetic anomalies related to major seismic events. </p>

scholarly journals ULF Pre-Seismic Geomagnetic Anomalous Signal Related to Mw8.1 Offshore Chiapas Earthquake, Mexico on 8 September 2017

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 29 ◽  
Author(s):  
Dragoș Armand Stănică ◽  
Dumitru Stănică
Keyword(s):  
Low Frequency ◽  
Random Variable ◽  
Seismic Signal ◽  
Polarization Parameter ◽  
Earthquake Nucleation ◽  
Anomalous Signal ◽  
Variable Equation ◽  
Band Pass ◽  
Geomagnetic Observations ◽  
The Relationship

In the last decade, the real time ground–based geomagnetic observations realized in correlation with the Vrancea seismicity in Romania, together with supplementary studies related to some earthquakes (Mw9.0 Tohoku, Japan on 11 March 2011 and Mw8.3 Coquimbo, Chile on 16 September 2015), enlarged our knowledge about the relationship between the pre-seismic anomalous phenomena and the final stage of the earthquake nucleation. To identify possible ultra-low-frequency (ULF) geomagnetic signals prior to the onset of an Mw8.1 earthquake, we retroactively analyzed the data collected on the interval 1 August–16 September 2017 at the Geomagnetic Observatories in Teoloyucan (TEO), Mexico and Tucson (TUC) USA, with the last taken as a reference. Daily mean distributions of the polarization parameter BPOL (geomagnetic polarization parameter) and standard deviation are obtained for both observatories using a fast Fourier transform (FFT) band-pass filtering in the ULF range (0.001–0.083 Hz). Further on, we investigated the singularity of the pre-seismic signal associated with an Mw8.1 earthquake and applied a statistical analysis based on a standardized random variable equation; results are presented as BPOL* time series on the interval 1–26 September. Finally, the hourly mean distribution, obtained as difference BPOL (TUC-TEO) on the interval 7–9 September emphasizes an anomalous signal with five hours before the onset of the Mw8.1 earthquake.

scholarly journals Variable demand model for periodically reviewing with allowing refunding parts of the orders

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
O. M. Hollah
Keyword(s):  
Field Study ◽  
Lead Time ◽  
Real Life ◽  
Random Variable ◽  
Inventory Level ◽  
Periodic Review ◽  
Demand Model ◽  
Time Intervals ◽  
Lagrange Multiplier Technique ◽  
Periodic Review Inventory

AbstractDepending on a field study for one of the largest iron and paints warehouses in Egypt, this paper presents a new multi-item periodic review inventory model considering the refunding quantity cost. Through this field study, we found that the inventory level is monitored periodically at equal time intervals. Returning a part of the goods that were previously ordered is permitted. Also, a shortage is permissible to occur despite having orders, and it is a combination of the backorder and lost sales. This model has been applied in both crisp and fuzzy environments since the fuzzy case is more suitable for real-life than crisp. The Lagrange multiplier technique is used for solving the restricted mathematical model. Here, the demand is a random variable that follows the normal distribution with zero lead-time. Finally, the model is followed by a real application to clarify the model and prove its efficiency.

Pre-seismic geomagnetic anomalous signature related to the Mw7.0 earthquake generated in the northern coastal zone of Samos island – Greece, on October 30, 2020

2021 ◽  
Author(s):  
Dumitru Stanica ◽  
Dragos Armand Stanica
Keyword(s):  
Time Series ◽  
Coastal Zone ◽  
Geomagnetic Field ◽  
Strong Earthquake ◽  
Random Variable ◽  
Strain Effect ◽  
Signal Identification ◽  
Variable Equation ◽  
Band Pass ◽  
Local Anomalies

<p>A strong earthquake of magnitude Mw7.0 struck the northern coastal zone of Samos Island, Aegean See, Greece, on October 30, 2020, at 11:51 UTC. This earthquake was felt at a wide area including Athens (at 270km) and city of Heraklion, Crete (at 320km), causing over 120 deaths and a lot of damages on houses, buildings and infrastructures mainly in Samos Island and Izmir (Turkey). With the aim to identify an anomalous geomagnetic signature before the onset of this earthquake, we have retrospectively analyzed the data collected, on the interval September 16 - October 31, 2020, at the two geomagnetic observatories, Pedeli (PEG)-Greece and Panagjurishte (PAG)-Bulgaria, by using the polarization parameter (BPOL) and the strain effect–related to geomagnetic signal identification. Thus, for the both observation sites (PEG and PAG), the daily mean distribution of the BPOL and its standard deviation (SD) are carried out using a FFT band-pass filtering in the ULF range (0.001-0.0083Hz). Further on, a statistical analysis based on a standardized random variable equation was applied for the following two particular cases: a) to assess on the both time series BPOL*(PEG) and BPOL*(PAG) the anomalous signature related to Mw7.0 earthquake; b) to differentiate transient local anomalies associated with Mw7.0 earthquake from the internal and external parts of the geomagnetic field, taking the PAG Observatory as reference. Finally, on the BPOL*(PEG-PAG) time series, carried out on the interval 1-31 October, 2020, a very clear anomaly of maximum, greater than 1.2SD, was detected on October 27, with 3days before the onset of Mw7.0 earthquake.</p>

scholarly journals Development of a real-time regional-inundation forecasting model for the inundation warning system

2013 ◽  
Vol 15 (4) ◽  
pp. 1391-1407 ◽  
Author(s):  
Gwo-Fong Lin ◽  
Hsuan-Yu Lin ◽  
Yang-Ching Chou
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Control Point ◽  
Warning System ◽  
Computational Method ◽  
Support Vector ◽  
Forecasting Model ◽  
Time Data ◽  
Inundation Maps ◽  
Proposed Model

Accurate forecasts of the inundation depth are necessary for inundation warning and mitigation. In this paper, a real-time regional forecasting model is proposed to yield 1- to 3-h lead time inundation maps. First, the K-means based cluster analysis is developed to group the inundation depths and to indentify the control points. Second, the support vector machine is used as the computational method to develop the point forecasting module to yield inundation forecasts for each control point. Third, based on the forecasted depths and the geographic information, the spatial expansion module is developed to expand the point forecasts to the spatial forecasts. An actual application to Siluo Township, Taiwan, is conducted to demonstrate the advantage of the proposed model. The results indicate that the proposed model can provide accurate inundation maps for 1- to 3-h lead times. The accurate long lead time forecasts can extend the lead time to allow sufficient time to take emergency measures. Furthermore, the proposed model is an efficient process that can be trained rapidly with real-time data and is more suitable to be integrated with the decision support system. In conclusion, the proposed modeling technique is expected to be useful to support the inundation warning systems.

OPTIMIZATION OF MARKOV SYSTEMS OF QUEUEING WITH WAITING TIME IN THE MATLAB SYSTEM

2017 ◽  
pp. 39-47
Author(s):  
Viktor Afonin ◽  
Vladimir Valer'evich Nikulin
Keyword(s):  
Queueing System ◽  
Queueing Systems ◽  
Random Variable ◽  
Model Systems ◽  
Queuing Systems ◽  
Input Flow ◽  
Continuous Random Variable ◽  
Input Stream ◽  
Time Intervals ◽  
Markov Systems

The article focuses on attempt to optimize two well-known Markov systems of queueing: a multichannel queueing system with finite storage, and a multichannel queueing system with limited queue time. In the Markov queuing systems, the intensity of the input stream of requests (requirements, calls, customers, demands) is subject to the Poisson law of the probability distribution of the number of applications in the stream; the intensity of service, as well as the intensity of leaving the application queue is subject to exponential distribution. In a Poisson flow, the time intervals between requirements are subject to the exponential law of a continuous random variable. In the context of Markov queueing systems, there have been obtained significant results, which are expressed in the form of analytical dependencies. These dependencies are used for setting up and numerical solution of the problem stated. The probability of failure in service is taken as a task function; it should be minimized and depends on the intensity of input flow of requests, on the intensity of service, and on the intensity of requests leaving the queue. This, in turn, allows to calculate the maximum relative throughput of a given queuing system. The mentioned algorithm was realized in MATLAB system. The results obtained in the form of descriptive algorithms can be used for testing queueing model systems during peak (unchanged) loads.

Safety Monitoring and Warning System for Subway Construction Workers using Wearable Technology

2019 ◽  
Vol 13 ◽  
Author(s):  
Jun-hua Chen ◽  
Da-hu Wang ◽  
Cun-yuan Sun
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Early Warning ◽  
Warning System ◽  
Safety Monitoring ◽  
Wearable Technology ◽  
Construction Workers ◽  
Great Success ◽  
Subway Construction ◽  
Monitoring And Early Warning

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

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