Forecasting the Start and End of Pollen Season in Madrid

Navares, Ricardo; Aznarte, José Luis

doi:10.1007/978-3-319-55789-2_27

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples include regression models [3,4], time series models [5], and process based phenological models [6]. In the last decade, machine learning techniques have been gaining importance due to the success of their applications [4,[7][8][9][10][11][12]. However, these techniques require a significant amount of data, and when dealing with pollen time series, where high concentrations are especially harmful when they are over 25 grains/m 3 [1], the data are incomplete during the full year ( Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Navares¹,

Aznarte²

2019

Atmosphere

Self Cite

View full text Add to dashboard Cite

Airborne pollen monitoring datasets sometimes exhibit gaps, even very long, either because of maintenance or because of a lack of expert personnel. Despite the numerous imputation techniques available, not all of them effectively include the spatial relations of the data since the assumption of missing-at-random is made. However, there are several techniques in geostatistics that overcome this limitation such as the inverse distance weighting and Gaussian processes or kriging. In this paper, a new method is proposed that utilizes convolutional neural networks. This method not only shows a competitive advantage in terms of accuracy when compared to the aforementioned techniques by improving the error by 5% on average, but also reduces execution training times by 90% when compared to a Gaussian process. To show the advantages of the proposal, 10%, 20%, and 30% of the data points are removed in the time series of a Poaceae pollen observation station in the region of Madrid, and the airborne concentrations from the remaining available stations in the network are used to impute the data removed. Even though the improvements in terms of accuracy are not significantly large, even if consistent, the gain in computational time and the flexibility of the proposed convolutional neural network allow field experts to adapt and extend the solution, for instance including meteorological variables, with the potential decrease of the errors reported in this paper.

show abstract