Forecasting Plantago pollen: improving feature selection through random forests, clustering, and Friedman tests

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

“…Even though only pollen observations were used in this study, mainly to compare the proposed solution with the benchmark IDW, the CNN provided the flexibility to include meteorological measures or predictions as input variables. There is evidence that including such variables [12,24] improves the estimations of airborne pollen concentrations. Moreover, these variables serve as a differential factor to mitigate under-and over-estimation of sudden high peaks during the main pollen season.…”

“…Even though there is extensive literature about computational intelligence techniques applied to pollen time series, such as random forests [7,12,23,24], artificial neural networks [9,10], and deep neural architectures [25], very few works have applied convolutional neural networks to time series. Nonetheless, CNNs have been extensively used in identifying and classifying pollen grains [26,27].…”

Recent developments in monitoring and modelling airborne pollen, a review

“…Random Forests differ slightly from the other methods in that it is an ensemble technique and involves constructing a series of decision trees. This method generates a number of regression trees relating to the sample data and then combines them by averaging (Navares & Aznarte 2020). This method has the added capability of determining the most important variables contributing to a model (Zewdie et al 2019a) and as such have been used in several studies to identify the most important variables for improving ML models (Navares & Aznarte 2017;Navares & Aznarte 2020).…”

“…This method generates a number of regression trees relating to the sample data and then combines them by averaging (Navares & Aznarte 2020). This method has the added capability of determining the most important variables contributing to a model (Zewdie et al 2019a) and as such have been used in several studies to identify the most important variables for improving ML models (Navares & Aznarte 2017;Navares & Aznarte 2020). Aside from this, RFs have been used to develop prediction models for high concentrations of different pollen types, such as Alnus, Betula and Corylus (Nowosad 2016;Nowosad et al 2016) and Poaceae (Navares & Aznarte 2017).…”

“…Pruning is another well documented algorithm used to limit the effect of model overfitting and is commonly applied to RFs (Singh et al 2013;Csépe et al 2014). Alternative methods to correct overfitting include bootstrap aggregation/bagging (Navares & Aznarte 2020;Soundiran et al 2019), gradient boosting (Dai et al 2019) andBayesian Regularization (Balram et al 2019;Zanotti et al 2019).…”