A quantitative evaluation of rip current appearance in Argus timex imagery: when and where does offshore flow correspond to visible features?

2020 ◽  
Author(s):  
Sarah Trimble ◽  
Allison Penko
Keyword(s):  
Machine Learning ◽  
Quantitative Evaluation ◽  
Radar Data ◽  
The United States ◽  
Surface Flow ◽  
Rip Current ◽  
Rip Currents ◽  
Video Cameras ◽  
Offshore Flow ◽  
Current Detection

<p>Modelling changes in nearshore bathymetry (<10m depth) is complicated by the nonlinear interactions between sediment, waves, and currents that can cause complex flow and transport patterns such as rip currents. Rip currents are of particular interest because of their implications for both sediment transport and beach-goer safety. An active area of research is using remote sensing (e.g., radar, video imagery) to estimate the existence and location of rip currents. Radar actively measures surface flow directions at high resolutions, however, the equipment can be expensive and difficult to set up. In contrast, video cameras are less expensive and more accessible, but can only provide passive observations that estimate derived surface quantities such as current speed and direction, and wave runup. Time exposure (timex) images from video cameras also provide information about the location of bright pixels (indications of breaking waves). Previous research has relied on the appearance of elongated, shore-normal regions of dark pixels (intersecting bright white regions) as a clear indicator of rip current presence, making timex images a prime candidate for automated detection of rip currents on beaches with video cameras installed. However, it is also known that rip currents vary widely in appearance, and that a better understanding of these parameters is necessary for automated rip current detection.</p><p>In this study, radar data and Argus camera imagery from the United States Army Corps of Engineers Field Research Facility at Duck, NC, USA were evaluated to determine how often radar measured offshore flow indicative of a rip current spatially correlates with dark, shore-normal features in the camera imagery. Radar data for two different times were processed to obtain surface current directions. Timex imagery from the video cameras on the same dates were evaluated with a machine learning algorithm   (Maryan et al. 2019) to objectively define the dark shore-normal features previously assumed to indicate rip currents’ existence within the imagery. A confusion matrix between these two datasets (surface flow direction and machine-identified rip current regions) confirms that dark, shore-normal features in the timex images are not always rip currents, and that offshore directed surface currents are not always visible as dark features in timex images. These results provide the first quantitative evaluation of how often rip current detections are missed and show that additional information is required for accurate automated rip current detection from camera imagery.</p><p>Further analysis will include using wind and wave data from field instruments at the site to reveal which conditions produce (1) offshore flow that is correlated with dark, shore-normal features in the timex imagery, (2) offshore flow that is not correlated with dark, shore-normal features in the timex imagery, and (3) dark, shore-normal features without focused offshore flow. This ongoing study could lead to the clarification of specific conditions under which the existence of rip currents can be correlated with a particular feature that machine learning techniques can be trained to recognize in camera imagery, thereby improving the accuracy of automated rip current detection. </p>

scholarly journals Machine learning analysis of lifeguard flag decisions and recorded rescues

2019 ◽  
Vol 19 (11) ◽  
pp. 2541-2549
Author(s):  
Chris Houser ◽  
Jacob Lehner ◽  
Nathan Cherry ◽  
Phil Wernette
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Machine Learning Techniques ◽  
Public Health Issue ◽  
Rip Currents ◽  
Effective Strategies ◽  
Wave Forcing ◽  
Learning Techniques ◽  
Yellow Flag ◽  
The Impact

Abstract. Rip currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags, and signs are important, and to varying degrees they are effective strategies to minimize risk to beach users. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow, and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard(s) monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag is not consistent with the beach user perception of the risk, which may increase the potential for rescues or drownings. In this study, machine learning is used to determine the potential for error in the flags used at Pensacola Beach and the impact of that error on the number of rescues. Results of a decision tree analysis indicate that the colour flag chosen by the lifeguards was different from what the model predicted for 35 % of days between 2004 and 2008 (n=396/1125). Days when there is a difference between the predicted and posted flag colour represent only 17 % of all rescue days, but those days are associated with ∼60 % of all rescues between 2004 and 2008. Further analysis reveals that the largest number of rescue days and total number of rescues are associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model predicted a green flag would be more appropriate based on the wind and wave forcing alone. While it is possible that the lifeguards were overly cautious, it is argued that they most likely identified a rip forced by a transverse-bar and rip morphology common at the study site. Regardless, the results suggest that beach users may be discounting lifeguard warnings if the flag colour is not consistent with how they perceive the surf hazard or the regional forecast. Results suggest that machine learning techniques have the potential to support lifeguards and thereby reduce the number of rescues and drownings.

scholarly journals Public perceptions of a rip current hazard education program: “Break the Grip of the Rip!”

2017 ◽  
Vol 17 (7) ◽  
pp. 1003-1024 ◽  
Author(s):  
Chris Houser ◽  
Sarah Trimble ◽  
Robert Brander ◽  
B. Chris Brewster ◽  
Greg Dusek ◽  
...  
Keyword(s):  
United States ◽  
Online Survey ◽  
Social Research ◽  
Current Knowledge ◽  
The United States ◽  
Rip Current ◽  
Rip Currents ◽  
Safety Education ◽  
Large Area ◽  
Education Material

Abstract. Rip currents pose a major global beach hazard; estimates of annual rip-current-related deaths in the United States alone range from 35 to 100 per year. Despite increased social research into beach-goer experience, little is known about levels of rip current knowledge within the general population. This study describes the results of an online survey to determine the extent of rip current knowledge across the United States, with the aim of improving and enhancing existing beach safety education material. Results suggest that the US-based Break the Grip of the Rip!® campaign has been successful in educating the public about rip current safety directly or indirectly, with the majority of respondents able to provide an accurate description of how to escape a rip current. However, the success of the campaign is limited by discrepancies between personal observations at the beach and rip forecasts that are broadcasted for a large area and time. It was the infrequent beach user that identified the largest discrepancies between the forecast and their observations. Since infrequent beach users also do not seek out lifeguards or take the same precautions as frequent beach users, it is argued that they are also at greatest risk of being caught in a dangerous situation. Results of this study suggest a need for the national campaign to provide greater focus on locally specific and verified rip forecasts and signage in coordination with lifeguards, but not at the expense of the successful national awareness program.

scholarly journals Estimations of rip current rescues and drowning in the United States

2019 ◽  
Vol 19 (2) ◽  
pp. 389-397 ◽  
Author(s):  
B. Chris Brewster ◽  
Richard E. Gould ◽  
Robert W. Brander
Keyword(s):  
United States ◽  
West Coast ◽  
Regional Variation ◽  
East Coast ◽  
Scientific Literature ◽  
The United States ◽  
Incident Reporting ◽  
Rip Current ◽  
Rip Currents ◽  
States Data

Abstract. Rip currents are the greatest hazard to swimmers on surf beaches, but due to a lack of consistent incident reporting in many countries, it is often difficult to quantify the number of rip-current-related rescues and drowning deaths occurring along surf beaches. This study examines this problem using rescue data reported to the United States Lifesaving Association (USLA) by surf beach rescuers from 1997 through 2016. These data were checked, corrected, and culled so that only data from surf beach rescue agencies that reported the primary cause of rescue were included. Results show that rip currents are the primary cause of 81.9 % of rescues on surf beaches, with regional variation from 75.3 % (East Coast) to 84.7 % (West Coast). These values are significantly higher than those previously reported in the scientific literature (e.g., 36.5 %, 53.7 %). Using this value as a proxy when examining overall surf beach drowning fatalities, it is suggested that more than 100 fatal drownings per year occur due to rip currents in the United States. However, it is clear that the United States data would benefit by an increase in the number of lifeguard agencies which report surf-related rescues by primary cause.

scholarly journals Public Perceptions of a Rip Current Hazard Education Program: Break the Grip of the Rip!

2017 ◽  
Author(s):  
Chris Houser ◽  
Sarah Trimble ◽  
Robert Brander ◽  
B. Chris Brewster ◽  
Greg Dusek ◽  
...  
Keyword(s):  
United States ◽  
Online Survey ◽  
Social Research ◽  
Current Knowledge ◽  
The United States ◽  
Rip Current ◽  
Rip Currents ◽  
Safety Education ◽  
Large Area ◽  
Education Material

Abstract. Rip currents pose a major global beach hazard; estimates of annual rip current related deaths in the United States alone range from 35 to 100 per year. Despite increased social research into beach-goer experience, little is known about levels of rip current knowledge within the general population. This study describes results of an online survey to determine the extent of rip current knowledge across the United States, with the aim of improving and enhancing existing beach safety education material. Results suggest that the Break the Grip of the Rip® campaign has been successful in educating the public about rip current safety directly or indirectly, with the majority of respondents able to provide an accurate description of how to escape a rip current. However, the success of the campaign is limited by discrepancies between personal observations at the beach and rip forecasts that are broadcasted for a large area and time. It was the infrequent beach user that identified the largest discrepancies between the forecast and their observations. Since infrequent beach users also do not seek out lifeguards or take the same precautions as frequent beach users, it is argued that they are also at greatest risk of being caught in a dangerous situation. Results of this study suggest a need for the national campaign to provide greater focus on locally specific and verified rip forecasts and signage in coordination with lifeguards, but not at the expense of the successful national awareness program.

scholarly journals Rip current rescues and drowning in the United States

2018 ◽  
Author(s):  
B. Chris Brewster ◽  
Richard E. Gould ◽  
Robert W. Brander
Keyword(s):  
United States ◽  
West Coast ◽  
Regional Variation ◽  
East Coast ◽  
Scientific Literature ◽  
The United States ◽  
Incident Reporting ◽  
Rip Current ◽  
Rip Currents ◽  
States Data

Abstract. Rip currents are the greatest hazard to swimmers on surf beaches, but due to a lack of consistent incident reporting in many countries, it is often difficult to quantify the number of rip current related rescues and drowning deaths occurring along surf beaches. This study uses rescue data reported to the United States Lifesaving Association (USLA) by surf beach lifeguards from 1997 through 2016 to provide an estimate of rip current related rescues in the United States. Results show that rip currents are the primary cause of 81.9 % of rescues on surf beaches, with regional variation from 75.3% (East Coast) to 84.7 % (West Coast). These values are significantly higher than those previously reported in the scientific literature. Using this value as a proxy when examining overall surf beach related drowning fatalities, it is suggested that an annual figure of 100 fatal drownings per year due to rip currents in the United States is possibly an under-estimate. However, it is clear that the United States data would benefit by an increase in the number of lifeguard agencies which report surf related rescues by primary cause.

A Database for Counterfeit Electronics and Automatic Defect Detection Based on Image Processing and Machine Learning

2016 ◽  
Author(s):  
Navid Asadizanjani ◽  
Sachin Gattigowda ◽  
Mark Tehranipoor ◽  
Domenic Forte ◽  
Nathan Dunn
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Integrated Circuits ◽  
Web Application ◽  
The United States ◽  
Global Market ◽  
Online Database ◽  
Ongoing Effort ◽  
Time Period ◽  
Physical Defects

Abstract Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

Race and Gender

2020 ◽  
pp. 251-269 ◽  
Author(s):  
Timnit Gebru
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
The United States ◽  
Error Rates ◽  
Political Factors ◽  
Recidivism Rates ◽  
Race And Gender ◽  
Decision Tools ◽  
And Gender ◽  
Technical Solutions

This chapter discusses the role of race and gender in artificial intelligence (AI). The rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial automated facial analysis systems have much higher error rates for dark-skinned women, while having minimal errors on light-skinned men. Moreover, a 2016 ProPublica investigation uncovered that machine learning–based tools that assess crime recidivism rates in the United States are biased against African Americans. Other studies show that natural language–processing tools trained on news articles exhibit societal biases. While many technical solutions have been proposed to alleviate bias in machine learning systems, a holistic and multifaceted approach must be taken. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.

scholarly journals Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

2021 ◽  
Vol 14 (5) ◽  
pp. 472
Author(s):  
Tyler C. Beck ◽  
Kyle R. Beck ◽  
Jordan Morningstar ◽  
Menny M. Benjamin ◽  
Russell A. Norris
Keyword(s):  
United States ◽  
Machine Learning ◽  
Drug Interactions ◽  
The United States ◽  
Structural Features ◽  
Physiochemical Properties ◽  
Drug Dosing ◽  
Therapeutic Outcomes ◽  
Cyp Inhibition ◽  
Cyp Inhibitors

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

scholarly journals Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 35
Author(s):  
Xiaodong Huang ◽  
Beth Ziniti ◽  
Michael H. Cosh ◽  
Michele Reba ◽  
Jinfei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Canopy Cover ◽  
The United States ◽  
Optical Data ◽  
Study Region ◽  
Model Based ◽  
Key Indicator ◽  
Ground Measurement ◽  
L Band

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

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