Artificial neural networks as a useful tool to predict the risk level of Betula pollen in the air
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Summary
The performance of the neural network with the validation set showed that the risk of the pollen level exceeding a certain threshold can be successfully forecasted using artificial neural networks, a widespread statistical tool useful for the study of problems associated with complex or poorly understood phenomena.
- Type
- article
- Published
- 2005-01-13
- Cited by
- 46
- References
- 51
- OpenAlex
- https://openalex.org/W15647908
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7056056
Keywords
Poetry, Reading (process), Art, Literature, History
References
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Cited by
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- Forecast models for the main features of the pollen season and daily average counts for allergic taxa in central Albania
- Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula
- THE ADVANCED STATISTICAL METHODS IN AEROBIOLOGICAL STUDIES
- Stepwise selection of functional covariates in forecasting peak levels of olive pollen
- Identification of NOx and Ozone Episodes and Estimation of Ozone by Statistical Analysis
- Changes in concentration of Alternaria and Cladosporium spores during summer storms
- Evaluation of atmospheric Poaceae pollen concentration using a neural network applied to a coastal Atlantic climate region
- Forecasting ragweed pollen characteristics with nonparametric regression methods over the most polluted areas in Europe
- Predicting Critical Micelle Concentration Values of Non-Ionic Surfactants by Using Artificial Neural Networks
- Role of Artificial Neural Networks in Dermatology
- The long‐range transport of birch (Betula) pollen from Poland and Germany causes significant pre‐season concentrations in Denmark
- Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
- A method for producing airborne pollen source inventories: an example of Ambrosia (ragweed) on the Pannonian Plain.
- Artificial neural network model of the relationship between Betula pollen and meteorological factors in Szczecin (Poland)
- A model to forecast the risk periods of Plantago pollen allergy by using the ANN methodology
- Temporal modelling and forecasting of the airborne pollen of Cupressaceae on the southwestern Iberian Peninsula
- Esters flash point prediction using artificial neural networks
- Modelling atmospheric concentrations of grass pollen using meteorological variables in Melbourne, Australia
- Predicting daily ragweed pollen concentrations using Computational Intelligence techniques over two heavily polluted areas in Europe.
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