Using machine learning to estimate atmospheric<i>Ambrosia</i>pollen concentrations in Tulsa, OK

Liu, Xun; Wu, Daji; Zewdie, Gebreab K.; Wijerante, Lakitha; Timms, Christopher I; Riley, A. H.; Levetin, Estelle; Lary, David J.

doi:10.1177/1178630217699399

Cited by 20 publications

(11 citation statements)

References 20 publications

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“…In recent years, the use of statistical- or machine-learning algorithms has increased in the social sciences. 1 For instance, to predict economic recession, Liu et al (2017) compared ordinary least-squares regression results with random forest regression results and obtained a considerably higher adjusted R -squared value with random forest regression compared with ordinary least-squares regression (Nyman and Ormerod 2017). In economics, a recent book overviews various statistical-learning algorithms for predicting economic growth and recession (Basuchoudhary, Bang, and Sen 2017).…”

Section: Introductionmentioning

confidence: 99%

The random forest algorithm for statistical learning

2020

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Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical- or machine-learning algorithm for prediction. In this article, we introduce a corresponding new command, rforest. We overview the random forest algorithm and illustrate its use with two examples: The first example is a classification problem that predicts whether a credit card holder will default on his or her debt. The second example is a regression problem that predicts the logscaled number of shares of online news articles. We conclude with a discussion that summarizes key points demonstrated in the examples.

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Section: Introductionmentioning

confidence: 99%

The random forest algorithm for statistical learning

2020

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“…The use of machine learning in this study builds on our heritage of using machine learning for sensing applications over the last two decades [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

“…Other authors have described, in detail, various configurations of autonomous robots, for example [ 1 , 2 , 3 , 4 , 5 , 6 ]. Here, we leverage our past experience over the last two decades in pioneering the use of machine learning for providing and calibrating remote sensing data products [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ] and use it to inform the design and operation of the robotic team.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning

Lary

Schaefer

Waczak

et al. 2021

Sensors

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This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and it is relevant to satellite calibration/validation and the creation of new remote sensing data products. A case study is described for the rapid characterisation of the aquatic environment, over a period of just a few minutes we acquired thousands of training data points. This training data allowed for our machine learning algorithms to rapidly learn by example and provide wide area maps of the composition of the environment. Along side these larger autonomous robots two smaller robots that can be deployed by a single individual were also deployed (a walking robot and a robotic hover-board), observing significant small scale spatial variability.

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“…The RF algorithm is a data-driven modeling approach,31, 32 and is applied herein in order to identify the level of relationship between one of the pollen monitoring stations and the symptom data. The hypothesis to be evaluated is that if one of the two stations is more influential in the description of the symptom data, then data from this would lead to better symptom modeling results in comparison to the data coming from the alternative station and being used as model input.…”

Section: Methodsmentioning

confidence: 99%

The evaluation of pollen concentrations with statistical and computational methods on rooftop and on ground level in Vienna – How to include daily crowd-sourced symptom data

Bastl

Karatzas

et al. 2019

World Allergy Organization Journal

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Background It is recommended to position pollen monitoring stations on rooftop level to assure a large catchment area and to gain data that are representative for a regional scale. Herein, an investigation of the representativeness of pollen concentrations was performed for 20 pollen types in the pollen seasons 2015–2016 in Vienna for rooftop and ground level and was compared with weather data and for the first time with symptom data. Methods The complete data set was analyzed with various statistical methods including Spearmen correlation, ANOVA, Kolmogorov–Smirnov test and logistic regression calculation: Odds ratio and Yule's Q values. Computational intelligence methods, namely Self Organizing Maps (SOMs) were employed that are capable of describing similarities and interdependencies in an effective way taking into account the U-matrix as well. The Random Forest algorithm was selected for modeling symptom data. Results The investigation of the representativeness of pollen concentrations on rooftop and ground level concerns the progress of the season, the peak occurrences and absolute quantities. Most taxa examined showed similar patterns (e.g. Betula ), while others showed differences in pollen concentrations exposure on different heights (e.g. the Poaceae family). Maximum temperature, mean temperature and humidity showed the highest influence among the weather parameters and daily pollen concentrations for the majority of taxa in both traps. Conclusion The rooftop trap was identified as the more adequate one when compared with the local symptom data. Results show that symptom data correlate more with pollen concentrations measured on rooftop than with those measured on ground level.

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Using machine learning to estimate atmosphericAmbrosiapollen concentrations in Tulsa, OK

Cited by 20 publications

References 20 publications

The random forest algorithm for statistical learning

The random forest algorithm for statistical learning

Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning

The evaluation of pollen concentrations with statistical and computational methods on rooftop and on ground level in Vienna – How to include daily crowd-sourced symptom data

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