Water Level Forecasting based on Deep Learning : A Use Case of Trinity River-Texas-The United States

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking.Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age.Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

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Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Neighborhood Features

10.1101/2020.10.12.20211755 ◽

2020 ◽

Author(s):

Joshua J. Levy ◽

Rebecca M. Lebeaux ◽

Anne G. Hoen ◽

Brock C. Christensen ◽

Louis J. Vaickus ◽

...

Keyword(s):

United States ◽

Deep Learning ◽

Mortality Rate ◽

Satellite Images ◽

The United States ◽

Mortality Rates ◽

Image Features ◽

County Level ◽

Crude Mortality Rate ◽

Crude Mortality

AbstractWhat is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?BackgroundFollowing a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.MethodsSatellite images were extracted with the Google Static Maps application programming interface for 430 counties representing approximately 68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.ResultsPredicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r=0.72). Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race and age. Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g. sidewalks, driveways and hiking trails) associated with lower mortality.ConclusionsThe application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

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Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries

10.1101/705855 ◽

2019 ◽

Author(s):

S. B. Choi ◽

J. Kim ◽

I. Ahn

Keyword(s):

United States ◽

Time Series ◽

Deep Learning ◽

Seasonal Influenza ◽

Prediction Models ◽

Time Lag ◽

The United States ◽

Surveillance Data ◽

Influenza Surveillance ◽

The U.S

AbstractTo identify countries that have seasonal patterns similar to the time series of influenza surveillance data in the United States and other countries, and to forecast the 2018–2019 seasonal influenza outbreak in the U.S. using linear regression, auto regressive integrated moving average, and deep learning. We collected the surveillance data of 164 countries from 2010 to 2018 using the FluNet database. Data for influenza-like illness (ILI) in the U.S. were collected from the Fluview database. This cross-correlation study identified the time lag between the two time-series. Deep learning was performed to forecast ILI, total influenza, A, and B viruses after 26 weeks in the U.S. The seasonal influenza patterns in Australia and Chile showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively. The R2 score of DNN models for ILI for validation set in 2015–2019 was 0.722 despite how hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S. in 2018–2019 may be later and less severe than those in 2017–2018, judging from the influenza activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and type-specific influenza activities for next season at 40th week. The correlation for seasonal influenza among Australia, Chile, and the U.S. could be used to decide on influenza vaccine strategy six months ahead in the U.S.

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Hype around artificial intelligence set to settle

Emerald Expert Briefings ◽

10.1108/oxan-db241630 ◽

2019 ◽

Keyword(s):

Artificial Intelligence ◽

United States ◽

Deep Learning ◽

The United States ◽

Major Research ◽

Content Type ◽

The Past ◽

Explainable Ai

Subject Prospect for artificial intelligence applications. Significance Artificial intelligence (AI) technologies, particularly those using 'deep learning', have in the past five years helped to automate many tasks previously outside the capabilities of computers. There are signs that the feverish pace of progress seen recently is slowing. Impacts Western legislation will make companies responsible for preventing decisions based on biased AI. Advances in 'explainable AI' will be rapid. China will be a major research player in AI technologies, alongside the United States, Japan and Europe.

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Cluster Analysis of SARS-CoV-2 Gene using Deep Learning Autoencoder: Gene Profiling for Mutations and Transitions

10.1101/2021.03.16.435601 ◽

2021 ◽

Author(s):

Jun Miyake ◽

Takaaki Sato ◽

Shunsuke Baba ◽

Hayato Nakamura ◽

Hirohiko Niioka ◽

...

Keyword(s):

United States ◽

Cluster Analysis ◽

Deep Learning ◽

The United States ◽

Gene Profiling ◽

Type B ◽

Type D ◽

Type C ◽

The U.S ◽

Over Time

We report on a method for analyzing the variant of coronavirus genes using autoencoder. Since coronaviruses have mutated rapidly and generated a large number of genotypes, an appropriate method for understanding the entire population is required. The method using autoencoder meets this requirement and is suitable for understanding how and when the variants emarge and disappear. For the over 30,000 SARS-CoV-2 ORF1ab gene sequences sampled globally from December 2019 to February 2021, we were able to represent a summary of their characteristics in a 3D plot and show the expansion, decline, and transformation of the virus types over time and by region. Based on ORF1ab genes, the SARS-CoV-2 viruses were classified into five major types (A, B, C, D, and E in the order of appearance): the virus type that originated in China at the end of 2019 (type A) practically disappeared in June 2020; two virus types (types B and C) have emerged in the United States and Europe since February 2020, and type B has become a global phenomenon. Type C is only prevalent in the U.S. and is suspected to be associated with high mortality, but this type also disappeared at the end of June. Type D is only found in Australia. Currently, the epidemic is dominated by types B and E.

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Visualization of Lake Mead Surface Area Changes from 1972 to 2009

10.32920/14639064.v1 ◽

2021 ◽

Author(s):

K. Wayne Forsythe ◽

Barbara Schatz ◽

Stephen J. Swales ◽

Lisa-Jen Ferrato ◽

David M. Atkinson

Keyword(s):

United States ◽

Water Level ◽

Surface Area ◽

Satellite Images ◽

Strong Relationship ◽

The United States ◽

Water Levels ◽

Lake Area ◽

Lake Mead ◽

Landsat Satellite

For most of the last decade, the south-western portion of the United States has experienced a severe and enduring drought. This has caused serious concerns about water supply and management in the region. In this research, 30 orthorectified Landsat satellite images from the United States Geological Service (USGS) Earth Explorer archive were analyzed for the 1972 to 2009 period. The images encompassed Lake Mead (a major reservoir in this region) and were examined for changes in water surface area. Decadal lake area minimums/maximums were achieved in 1972/1979, 1981/1988, 1991/1998, and 2009/2000. The minimum lake area extent occurred in 2009 (356.4 km2), while the maximum occurred in 1998 (590.6 km2). Variable trends in water level and lake area were observed throughout the analysis period, however progressively lower values were observed since 2000. The Landsat derived lake areas show a very strong relationship with actual measured water levels at the Hoover Dam. Yearly water level variations at the dam vary minimally from the satellite derived estimates. A complete (yearly) record of satellite images may have helped to reduce the slight deviations in the time series.

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Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection

Remote Sensing ◽

10.3390/rs13234790 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4790

Author(s):

Qi Zhang ◽

Linlin Ge ◽

Ruiheng Zhang ◽

Graciela Isabel Metternicht ◽

Chang Liu ◽

...

Keyword(s):

United States ◽

Deep Learning ◽

Fire Detection ◽

The United States ◽

Fire Service ◽

Detection Accuracy ◽

Processing Efficiency ◽

High Resolution Data ◽

Active Fire ◽

Sentinel 2

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019–2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km2 (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans.

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Automated Water Level Monitoring at the Continental Scale from ICESat-2 Photons

Remote Sensing ◽

10.3390/rs13183631 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3631

Author(s):

Austin Madson ◽

Yongwei Sheng

Keyword(s):

United States ◽

Water Level ◽

Cluster Computing ◽

Mean Squared Error ◽

The United States ◽

United States Geological Survey ◽

Continental Scale ◽

Water Level Changes ◽

Lakes And Reservoirs ◽

The Mean

Of the approximately 6700 lakes and reservoirs larger than 1 km2 in the Contiguous United States (CONUS), only ~430 (~6%) are actively gaged by the United States Geological Survey (USGS) or their partners and are available for download through the National Water Information System database. Remote sensing analysis provides a means to fill in these data gaps in order to glean a better understanding of the spatiotemporal water level changes across the CONUS. This study takes advantage of two-plus years of NASA’s ICESat-2 (IS-2) ATLAS photon data (ATL03 products) in order to derive water level changes for ~6200 overlapping lakes and reservoirs (>1 km2) in the CONUS. Interactive visualizations of large spatial datasets are becoming more commonplace as data volumes for new Earth observing sensors have markedly increased in recent years. We present such a visualization created from an automated cluster computing workflow that utilizes tens of billions of ATLAS photons which derives water level changes for all of the overlapping lakes and reservoirs in the CONUS. Furthermore, users of this interactive website can download segmented and clustered IS-2 ATL03 photons for each individual waterbody so that they may run their own analysis. We examine ~19,000 IS-2 derived water level changes that are spatially and temporally coincident with water level changes from USGS gages and find high agreement with our results as compared to the in situ gage data. The mean squared error (MSE) and the mean absolute error (MAE) between these two products are 1 cm and 6 cm, respectively.

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“We'll Put a Boot in Your Ass, It's the American Way”

Advances in Educational Marketing, Administration, and Leadership - Preventing and Reducing Violence in Schools and Society ◽

10.4018/978-1-7998-4072-5.ch005 ◽

2021 ◽

pp. 85-114

Author(s):

Evan Renfro ◽

Jayme Neiman Renfro

Keyword(s):

United States ◽

Popular Culture ◽

Case Studies ◽

The United States ◽

Popular Media ◽

The Other ◽

Use Case ◽

Television Film ◽

American Way ◽

Trump Administration

Since before the founding of the United States through slavery, the extermination of the native populace, war after war, regime overthrow, and more wars, popular media have been used to stir resentments and produce violent fantasies in the general citizenry that often allow for policies of actual violence to be applied against “the other.” This chapter will analyze the affective coordinates of this system in the post-9/11 context, focusing especially on how nationalist-jingoism has now triumphed in the age of the Trump Administration. Crucial interrogations addressed in this chapter include: Why are white southern/rural males particularly susceptible to popular culture induced affective violence? What are the mechanics of profit and neoliberal imperatives of this structure? What is new about the linkage of these phenomena with the first Twitter-President? In pursuing these questions, the authors will use case studies involving the popular media vectors of television, film, and music.

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