scholarly journals Predicting wildfire burned area in South Central US using integrated machine learning techniques

2019 ◽  
Author(s):  
Sing-Chun Wang ◽  
Yuxuan Wang
Keyword(s):  
United States ◽  
Machine Learning ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Environmental Drivers ◽  
Fire Season ◽  
Burned Area ◽  
Environmental Controls ◽  
South Central

Abstract. Occurrences of devastating wildfires have been on the rise in the United States for the past decades. While the environmental controls, including weather, climate, and fuels, are known to play important roles in controlling wildfires, the interrelationships between fires and the environmental controls are highly complex and may not be well represented by traditional parametric regressions. Here we develop a model integrating multiple machine learning algorithms to predict gridded monthly wildfire burned area during 2002–2015 over the South Central United States and identify the relative importance of the environmental drivers on the burned area for both the winter-spring and summer fire seasons of that region. The developed model is able to alleviate the issue of unevenly-distributed burned area data and achieve a cross-validation (CV) R2 value of 0.42 and 0.40 for the two fire seasons. For the total burned area over the study domain, the model can explain 50 % and 79 % of interannual total burned area for the winter-spring and summer fire season, respectively. The prediction model ranks relative humidity (RH) anomalies and preceding months’ drought severity as the top two most important predictors on the gridded burned area for both fire seasons. Sensitivity experiments with the model show that the effect of climate change represented by a group of climate-anomaly variables contributes the most to the burned area for both fire seasons. Antecedent fuel amount and conditions are found to outweigh weather effects for the burned area in the winter-spring fire season, while the current-month fire weather is more important for the summer fire season likely due to the controlling effect of weather on fuel moisture in this season. This developed model allows us to predict gridded burned area and to access specific fire management strategies for different fire mechanisms in the two seasons.

scholarly journals Quantifying the effects of environmental factors on wildfire burned area in the south central US using integrated machine learning techniques

2020 ◽  
Vol 20 (18) ◽  
pp. 11065-11087
Author(s):  
Sally S.-C. Wang ◽  
Yuxuan Wang
Keyword(s):  
United States ◽  
Machine Learning ◽  
The United States ◽  
Environmental Drivers ◽  
Fire Season ◽  
Spatial And Temporal Variation ◽  
Burned Area ◽  
The South ◽  
Environmental Controls ◽  
South Central

Abstract. Occurrences of devastating wildfires have been increasing in the United States for the past decades. While some environmental controls, including weather, climate, and fuels, are known to play important roles in controlling wildfires, the interrelationships between these factors and wildfires are highly complex and may not be well represented by traditional parametric regressions. Here we develop a model consisting of multiple machine learning algorithms to predict 0.5∘×0.5∘ gridded monthly wildfire burned area over the south central United States during 2002–2015 and then use this model to identify the relative importance of the environmental drivers on the burned area for both the winter–spring and summer fire seasons of that region. The developed model alleviates the issue of unevenly distributed burned-area data, predicts burned grids with area under the curve (AUC) of 0.82 and 0.83 for the two seasons, and achieves temporal correlations larger than 0.5 for more than 70 % of the grids and spatial correlations larger than 0.5 (p<0.01) for more than 60 % of the months. For the total burned area over the study domain, the model can explain 50 % and 79 % of the observed interannual variability for the winter–spring and summer fire season, respectively. Variable importance measures indicate that relative humidity (RH) anomalies and preceding months' drought severity are the two most important predictor variables controlling the spatial and temporal variation in gridded burned area for both fire seasons. The model represents the effect of climate variability by climate-anomaly variables, and these variables are found to contribute the most to the magnitude of the total burned area across the whole domain for both fire seasons. In addition, antecedent fuel amounts and conditions are found to outweigh the weather effects on the amount of total burned area in the winter–spring fire season, while fire weather is more important for the summer fire season likely due to relatively sufficient vegetation in this season.

scholarly journals A Multinetwork and Machine Learning Examination of Structure and Content in the United States Code

2021 ◽  
Vol 8 ◽  
Author(s):  
Keith Carlson ◽  
Faraz Dadgostari ◽  
Michael A. Livermore ◽  
Daniel N. Rockmore
Keyword(s):  
United States ◽  
Machine Learning ◽  
Topic Model ◽  
Citation Network ◽  
The United States ◽  
Hierarchical Organization ◽  
Machine Learning Techniques ◽  
Prediction Task ◽  
Network Layers ◽  
United States Code

This paper introduces a novel linked structure-content representation of federal statutory law in the United States and analyzes and quantifies its structure using tools and concepts drawn from network analysis and complexity studies. The organizational component of our representation is based on the explicit hierarchical organization within the United States Code (USC) as well an embedded cross-reference citation network. We couple this structure with a layer of content-based similarity derived from the application of a “topic model” to the USC. The resulting representation is the first that explicitly models the USC as a “multinetwork” or “multilayered network” incorporating hierarchical structure, cross-references, and content. We report several novel descriptive statistics of this multinetwork. These include the results of this first application of the machine learning technique of topic modeling to the USC as well as multiple measures articulating the relationships between the organizational and content network layers. We find a high degree of assortativity of “titles” (the highest level hierarchy within the USC) with related topics. We also present a link prediction task and show that machine learning techniques are able to recover information about structure from content. Success in this prediction task has a natural interpretation as indicating a form of mutual information. We connect the relational findings between organization and content to a measure of “ease of search” in this large hyperlinked document that has implications for the ways in which the structure of the USC supports (or doesn’t support) broad useful access to the law. The measures developed in this paper have the potential to enable comparative work in the study of statutory networks that ranges across time and geography.

scholarly journals Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach

10.2196/18401 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18401
Author(s):  
Jane M Zhu ◽  
Abeed Sarker ◽  
Sarah Gollust ◽  
Raina Merchant ◽  
David Grande
Keyword(s):  
Public Health ◽  
United States ◽  
Machine Learning ◽  
Public Health Education ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Care Organization ◽  
The Public ◽  
The Mean

Background Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. Objective We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public. Methods Using Twitter’s public application programming interface, we collected 158,714 public posts (“tweets”) from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 broad categories of content, and these coded tweets were used to train supervised machine learning algorithms to classify all collected posts. Results We identified 15 state Medicaid agencies and 81 Medicaid MCOs on Twitter. The mean number of followers was 1784, the mean number of those followed was 542, and the mean number of posts was 2476. Approximately 39% of tweets came from just 10 accounts. Of all posts, 39.8% (63,168/158,714) were classified as general public health education and outreach; 23.5% (n=37,298) were about specific Medicaid policies, programs, services, or events; 18.4% (n=29,203) were organizational promotion of staff and activities; and 11.6% (n=18,411) contained general news and news links. Only 4.5% (n=7142) of posts were responses to specific questions, concerns, or complaints from the public. Conclusions Twitter has the potential to enhance community building, beneficiary engagement, and public health outreach, but appears to be underutilized by the Medicaid program.

scholarly journals WHAT PERSONALISATION CAN DO FOR YOU! OR, HOW TO DO RACIAL DISCRIMINATION WITHOUT RACE

2021 ◽  
Author(s):  
Scott Wark ◽  
Thao Phan
Keyword(s):  
United States ◽  
Machine Learning ◽  
Asian American ◽  
Language Use ◽  
The United States ◽  
Machine Learning Techniques ◽  
Life And Death ◽  
Demographic Group ◽  
Learning Techniques ◽  
Inductive Machine Learning

Between 2016 and 2020, Facebook allowed advertisers in the United States to target their advertisements using three broad “ethnic affinity” categories: “African American,” “U.S.-Hispanic,” and “Asian American.” This paper uses the life and death of these “ethnic affinity” categories to argue that they exemplify a novel mode of racialisation made possible by machine learning techniques. These categories worked by analysing users’ preferences and behaviour: they were supposed to capture an “affinity” for a broad demographic group, rather than registering membership of that group. That is, they were supposed to allow advertisers to “personalise” content for users depending on behaviourally determined affinities. We argue that, in effect, Facebook’s ethnic affinity categories were supposed to operationalise a “post-racial” mode of categorising users. But the paradox of personalisation is that in order to apprehend users as individuals, platforms must first assemble them into groups based on their likenesses with other individuals. This article uses an analysis of these categories to argue that even in the absence of data on a user’s race—even after the demise of the categories themselves—users can still be subject to techniques of inclusion or exclusion for discriminatory ends. The inductive machine learning techniques that platforms like Facebook employ to classify users generate “proxies,” like racialised preferences or language use, as racialising substitutes. This article concludes by arguing that Facebook’s ethnic affinity categories in fact typify novel modes of racialisation today.

scholarly journals ESTIMATING CORN YIELD IN THE UNITED STATES WITH MODIS EVI AND MACHINE LEARNING METHODS

2016 ◽  
Vol III-8 ◽  
pp. 131-136 ◽  
Author(s):  
K. Kuwata ◽  
R. Shibasaki
Keyword(s):  
Neural Network ◽  
United States ◽  
Machine Learning ◽  
Crop Yield ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Corn Yield ◽  
County Level ◽  
Entire Area ◽  
Modis Evi

Satellite remote sensing is commonly used to monitor crop yield in wide areas. Because many parameters are necessary for crop yield estimation, modelling the relationships between parameters and crop yield is generally complicated. Several methodologies using machine learning have been proposed to solve this issue, but the accuracy of county-level estimation remains to be improved. In addition, estimating county-level crop yield across an entire country has not yet been achieved. In this study, we applied a deep neural network (DNN) to estimate corn yield. We evaluated the estimation accuracy of the DNN model by comparing it with other models trained by different machine learning algorithms. We also prepared two time-series datasets differing in duration and confirmed the feature extraction performance of models by inputting each dataset. As a result, the DNN estimated county-level corn yield for the entire area of the United States with a determination coefficient (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt;) of 0.780 and a root mean square error (&lt;i&gt;RMSE&lt;/i&gt;) of 18.2 bushels/acre. In addition, our results showed that estimation models that were trained by a neural network extracted features from the input data better than an existing machine learning algorithm.

Collusive Algorithms as Mere Tools, Super-tools or Legal Persons

2019 ◽  
Author(s):  
Guan Zheng ◽  
Hong Wu
Keyword(s):  
United States ◽  
Machine Learning ◽  
Learning Algorithms ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Tacit Collusion ◽  
Antitrust Law ◽  
Strong Argument ◽  
Market Pricing ◽  
Distinct Features

Abstract The widespread use of algorithmic technologies makes rules on tacit collusion, which are already controversial in antitrust law, more complicated. These rules have obvious limitations in effectively regulating algorithmic collusion. Although some scholars and practitioners within antitrust circles in the United States, Europe and beyond have taken notice of this problem, they have failed to a large extent to make clear its specific manifestations, root causes, and effective legal solutions. In this article, the authors make a strong argument that it is no longer appropriate to regard algorithms as mere tools of firms, and that the distinct features of machine learning algorithms as super-tools and as legal persons may inevitably bring about two new cracks in antitrust law. This article clarifies the root causes why these rules are inapplicable to a large extent to algorithmic collusion particularly in the case of machine learning algorithms, classifies the new legal cracks, and provides sound legal criteria for the courts and competition authorities to assess the legality of algorithmic collusion much more accurately. More importantly, this article proposes an efficacious solution to revive the market pricing mechanism for the purposes of resolving the two new cracks identified in antitrust law.

scholarly journals Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Jane M Zhu ◽  
Abeed Sarker ◽  
Sarah Gollust ◽  
Raina Merchant ◽  
David Grande
Keyword(s):  
Public Health ◽  
United States ◽  
Machine Learning ◽  
Public Health Education ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Care Organization ◽  
The Public ◽  
The Mean

BACKGROUND Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. OBJECTIVE We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public. METHODS Using Twitter’s public application programming interface, we collected 158,714 public posts (“tweets”) from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 broad categories of content, and these coded tweets were used to train supervised machine learning algorithms to classify all collected posts. RESULTS We identified 15 state Medicaid agencies and 81 Medicaid MCOs on Twitter. The mean number of followers was 1784, the mean number of those followed was 542, and the mean number of posts was 2476. Approximately 39% of tweets came from just 10 accounts. Of all posts, 39.8% (63,168/158,714) were classified as general public health education and outreach; 23.5% (n=37,298) were about specific Medicaid policies, programs, services, or events; 18.4% (n=29,203) were organizational promotion of staff and activities; and 11.6% (n=18,411) contained general news and news links. Only 4.5% (n=7142) of posts were responses to specific questions, concerns, or complaints from the public. CONCLUSIONS Twitter has the potential to enhance community building, beneficiary engagement, and public health outreach, but appears to be underutilized by the Medicaid program.

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