ArticleEnvironmental monitoring and assessment2019
Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters.
Article in Environmental monitoring and assessment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 18 citations in OpenAlex.
- An advanced framework for predicting pollen outbreaks using multi-input multi-output temporal convolutional networks and firefly algorithm.Scientific reports · 2026Article
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Geospatial technology in environmental health applications.Environmental monitoring and assessment · 2019Article
- Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.Environmental monitoring and assessment · 2019Article
- Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast AirborneInternational journal of environmental research and public health · 2019Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant trigger for several of these allergic diseases. Ambrosia (ragweed) is known for its abundant production of pollen and its potent allergic effect in North America. Hence, estimating and predicting the daily atmospheric concentration of pollen (ragweed pollen in particular) is useful for both people with allergies and for the health professionals who care for them. In this study, we show that a suite of variables including meteorological and land surface parameters, as well as next-generation radar (NEXRAD) measurements together with machine learning can be used to estimate successfully the daily pollen concentration. The supervised machine learning approaches we used included random forests, neural networks, and support vector machines. The performance of the training is independently validated using 10% of the data partitioned using the holdout cross-validation method from the original dataset. The random forests (R= 0.61, R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.