Real-Time Performance of the PLUM Earthquake Early Warning Method during the 2019 M 6.4 and 7.1 Ridgecrest, California, Earthquakes

2020 ◽  
Vol 110 (4) ◽  
pp. 1887-1903 ◽  
Author(s):  
Sarah E. Minson ◽  
Jessie K. Saunders ◽  
Julian J. Bunn ◽  
Elizabeth S. Cochran ◽  
Annemarie S. Baltay ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Cost Benefit Analysis ◽  
Protective Action ◽  
Cost Benefit ◽  
Earthquake Early Warning ◽  
Data Availability ◽  
High Data ◽  
Trade Offs ◽  
Warning Time

ABSTRACT We evaluate the timeliness and accuracy of ground-motion-based earthquake early warning (EEW) during the July 2019 M 6.4 and 7.1 Ridgecrest earthquakes. In 2018, we began retrospective and internal real-time testing of the propagation of local undamped motion (PLUM) method for earthquake warning in California, Oregon, and Washington, with the potential that PLUM might one day be included in the ShakeAlert EEW system. A real-time version of PLUM was running on one of the ShakeAlert EEW system’s development servers at the time of the 2019 Ridgecrest sequence, allowing us to evaluate the timeliness and accuracy of PLUM’s warnings for the M 6.4 and 7.1 mainshocks in real time with the actual data availability and latencies of the operational ShakeAlert EEW system. The latter is especially important because high-data latencies during the M 7.1 earthquake degraded ShakeAlert’s performance. PLUM proved to be largely immune to these latencies. In this article, we present a retrospective analysis of PLUM performance and explore three potential regional alerting strategies ranging from spatially large regions (counties), to moderate-size regions (National Weather Service public forecast zones), to high-spatial specificity (50 km regular geographic grid). PLUM generated initial shaking forecasts for the two mainshocks 5 and 6 s after their respective origin times, and faster than the ShakeAlert system’s first alerts. PLUM was also able to accurately forecast shaking across southern California for all three alerting strategies studied. As would be expected, a cost-benefit analysis of each approach illustrates trade-offs between increasing warning time and minimizing the area receiving unneeded alerts. Choosing an optimal alerting strategy requires knowledge of users’ false alarm tolerance and minimum required warning time for taking protective action, as well as the time required to distribute alerts to users.

scholarly journals Real Time Electrical Energy Monitoring and Cost Benefit Analysis using Smart Meter

2020 ◽  
Vol 8 (6S) ◽  
pp. 156-160
Keyword(s):  
Real Time ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Electrical Energy ◽  
Energy Resources ◽  
Energy Utilization ◽  
Daily Lives ◽  
New Energy ◽  
The Cost ◽  
User Friendly

Energy is an essential component in supporting people’s daily lives and is a significant economical element in development of the country. The eventual depletion of conventional energy resources and their harmful impacts on environment as well as the rising energy costs and the limitations of new energy resources and technologies have pushed efficient energy management to the top of the agenda. But how the energy utilization can be managed? A simple answer to this is viable and real time metering, which enables calculation of run time energy consumption and obtaining the real-time as well as cumulative cost. In this research an Innovative hardware and IoT based solution to this problem is availed that could provide live information related to consumption of electricity by various appliances. The methodology used in this research is mainly based on a hardware tool named Elite 440 which is a meter and provides the data about various electrical parameters. This data so obtained is made visible on the dashboard in a user friendly. The data so visible includes various parameters like voltage, current, power factor etc. Also the data so obtained on the dashboard gets updated in each five minutes and simultaneously the cost gets updated which makes it real time monitoring System.

scholarly journals COVID-19: Rethinking the Lockdown Groupthink

2021 ◽  
Vol 9 ◽  
Author(s):  
Ari R. Joffe
Keyword(s):  
Public Health ◽  
High Risk ◽  
Cost Benefit Analysis ◽  
Health Care Systems ◽  
Herd Immunity ◽  
Cost Benefit ◽  
Risk Groups ◽  
School Closures ◽  
Trade Offs ◽  
Care Systems

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused the Coronavirus Disease 2019 (COVID-19) worldwide pandemic in 2020. In response, most countries in the world implemented lockdowns, restricting their population's movements, work, education, gatherings, and general activities in attempt to “flatten the curve” of COVID-19 cases. The public health goal of lockdowns was to save the population from COVID-19 cases and deaths, and to prevent overwhelming health care systems with COVID-19 patients. In this narrative review I explain why I changed my mind about supporting lockdowns. The initial modeling predictions induced fear and crowd-effects (i.e., groupthink). Over time, important information emerged relevant to the modeling, including the lower infection fatality rate (median 0.23%), clarification of high-risk groups (specifically, those 70 years of age and older), lower herd immunity thresholds (likely 20–40% population immunity), and the difficult exit strategies. In addition, information emerged on significant collateral damage due to the response to the pandemic, adversely affecting many millions of people with poverty, food insecurity, loneliness, unemployment, school closures, and interrupted healthcare. Raw numbers of COVID-19 cases and deaths were difficult to interpret, and may be tempered by information placing the number of COVID-19 deaths in proper context and perspective relative to background rates. Considering this information, a cost-benefit analysis of the response to COVID-19 finds that lockdowns are far more harmful to public health (at least 5–10 times so in terms of wellbeing years) than COVID-19 can be. Controversies and objections about the main points made are considered and addressed. Progress in the response to COVID-19 depends on considering the trade-offs discussed here that determine the wellbeing of populations. I close with some suggestions for moving forward, including focused protection of those truly at high risk, opening of schools, and building back better with a economy.

Optimizing Earthquake Early Warning Alert Distance Strategies Using the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, Earthquakes

2020 ◽  
Vol 110 (4) ◽  
pp. 1872-1886 ◽  
Author(s):  
Jessie K. Saunders ◽  
Brad T. Aagaard ◽  
Annemarie S. Baltay ◽  
Sarah E. Minson
Keyword(s):  
Ground Motion ◽  
Early Warning ◽  
Ground Motions ◽  
Protective Action ◽  
Warning System ◽  
Prediction Equation ◽  
Earthquake Early Warning ◽  
Source Characterization ◽  
Earthquake Early Warning System ◽  
Alert Distance

ABSTRACT The ShakeAlert earthquake early warning system aims to alert people who experience modified Mercalli intensity (MMI) IV+ shaking during an earthquake using source estimates (magnitude and location) to estimate median-expected peak ground motions with distance, then using these ground motions to determine median-expected MMI and thus the extent of MMI IV shaking. Because median ground motions are used, even if magnitude and location are correct, there will be people outside the alert region who experience MMI IV shaking but do not receive an alert (missed alerts). We use 91,000 “Did You Feel It?” survey responses to the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, earthquakes to determine which ground-motion to intensity conversion equation (GMICE) best fits median MMI with distance. We then explore how incorporating uncertainty from the ground-motion prediction equation and the GMICE in the alert distance calculation can produce more accurate MMI IV alert regions for a desired alerting strategy (e.g., aiming to alert 95% of people who experience MMI IV+ shaking), assuming accurate source characterization. Without incorporating ground-motion uncertainties, we find MMI IV alert regions using median-expected ground motions alert fewer than 20% of the population that experiences MMI IV+ shaking. In contrast, we find >94% of the people who experience MMI IV+ shaking can be included in the MMI IV alert region when two standard deviations of ground-motion uncertainty are included in the alert distance computation. The optimal alerting strategy depends on the false alert tolerance of the community due to the trade-off between minimizing missed and false alerts. This is especially the case for situations like the Mw 6.4 earthquake when alerting 95% of the 5 million people who experience MMI IV+ also results in alerting 14 million people who experience shaking below this level and do not need to take protective action.

Too-Late Warnings by Estimating Mw: Earthquake Early Warning in the Near-Fault Region

2020 ◽  
Vol 110 (3) ◽  
pp. 1276-1288
Author(s):  
Mitsuyuki Hoshiba
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Strong Motion ◽  
Earthquake Early Warning ◽  
Motion Generation ◽  
Near Fault ◽  
Time Gap ◽  
Fault Region ◽  
Strong Ground

ABSTRACT Earthquake early warning (EEW) systems aim to provide advance warnings of impending strong ground shaking. Many EEW systems are based on a strategy in which precise and rapid estimates of source parameters, such as hypocentral location and moment magnitude (Mw), are used in a ground-motion prediction equation (GMPE) to predict the strength of ground motion. For large earthquakes with long rupture duration, the process is repeated, and the prediction is updated in accordance with the growth of Mw during the ongoing rupture. However, in some regions near the causative fault this approach leads to late warnings, because strong ground motions often occur during earthquake ruptures before Mw can be confirmed. Mw increases monotonically with elapsed time and reaches its maximum at the end of rupture, and ground motion predicted by a GMPE similarly reaches its maximum at the end of rupture, but actual generation of strong motion is earlier than the end of rupture. A time gap between maximum Mw and strong-motion generation is the first factor contributing to late warnings. Because this time gap exists at any point of time during the rupture, a late warning is inherently caused even when the growth of Mw can be monitored in real time. In the near-fault region, a weak subevent can be the main contributor to strong ground motion at a site if the distance from the subevent to the site is small. A contribution from a weaker but nearby subevent early in the rupture is the second factor contributing to late warnings. Thus, an EEW strategy based on rapid estimation of Mw is not suitable for near-fault regions where strong shaking is usually recorded. Real-time monitoring of ground motion provides direct information for real-time prediction for these near-fault locations.

Cost–benefit and water resources policy: a survey

Water Policy ◽  
2011 ◽  
Vol 14 (2) ◽  
pp. 250-280 ◽  
Author(s):  
Frank A. Ward
Keyword(s):  
Water Policy ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Benefit Analysis ◽  
Trade Offs ◽  
Wide Range ◽  
Recent Developments ◽  
The Difference ◽  
Policy Cost ◽  
The Right

This paper reviews recent developments in cost–benefit analysis for water policy researchers who wish to understand the applications of economic principles to inform emerging water policy debates. The cost–benefit framework can provide a comparison of total economic gains and losses resulting from a proposed water policy. Cost–benefit analysis can provide decision-makers with a comparison of the impacts of two or more water policy options using methods that are grounded in time-tested economic principles. Economic efficiency, measured as the difference between added benefits and added costs, can inform water managers and the public of the economic impacts of water programs to address peace, development, health, the environment, climate and poverty. Faced by limited resources, cost–benefit analysis can inform policy choices by summarizing trade-offs involved in designing, applying, or reviewing a wide range of water programs. The data required to conduct a cost–benefit analysis are often poor but the steps needed to carry out that analysis require posing the right questions.

End-to-end PGA estimation for earthquake early warning using transformer networks

2020 ◽  
Author(s):  
Jannes Münchmeyer ◽  
Dino Bindi ◽  
Ulf Leser ◽  
Frederik Tilmann
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Lead Times ◽  
Ground Shaking ◽  
Target Sites ◽  
End To End

<p>The key task of earthquake early warning is to provide timely and accurate estimates of the ground shaking at target sites. Current approaches use either source or propagation based methods. Source based methods calculate fast estimates of the earthquake source parameters and apply ground motion prediction equations to estimate shaking. They suffer from saturation effects for large events, simplified assumptions and the need for a well known hypocentral location, which usually requires arrivals at multiple stations. Propagation based methods estimate levels of shaking from the shaking at neighboring stations and therefore have short warning times and possibly large blind zones. Both methods only use specific features from the waveform. In contrast, we present a multi-station neural network method to estimate horizontal peak ground acceleration (PGA) anywhere in the target region directly from raw accelerometer waveforms in real time.</p><p>The three main components of our model are a convolutional neural network (CNN) for extracting features from the single-station three-component accelerograms, a transformer network for combining features from multiple stations and for transferring them to the target site features and a mixture density network to generate probabilistic PGA estimates. By using a transformer network, our model is able to handle a varying set and number of stations as well as target sites. We train our model end-to-end using recorded waveforms and PGAs. We use data augmentation to enable the model to provide estimations at targets without waveform recordings. Starting with the arrival of a P wave at any station of the network, our model issues real-time predictions at each new sample. The predictions are Gaussian mixtures, giving estimates of both expected value and uncertainties. The model can be used to predict PGA at specific target sites, as well as to generate ground motion maps.</p><p>We analyze the model on two strong motion data sets from Japan and Italy in terms of standard deviation and lead times. Through the probabilistic predictions we are able to give lead times for different levels of uncertainty and ground shaking. This allows to control the ratio of missed detections to false alerts. Preliminary analysis suggest that for levels between 1%g and 10%g our model achieves multi-second lead times even for the closest stations at a false-positive rate below 25%. For an example event at 50 km depth, lead times at the closest stations with epicentral distances below 20 km are 6 s and 7.5 s. This suggests that our model is able to effectively use the difference between P and S travel time and accurately assess the future level of ground shaking from the first parts of the P wave. It additionally makes effective use of the information contained in the absence of signal at other stations.</p>

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