What are the most important variables for Poaceae airborne pollen forecasting?

Navares, Ricardo; Aznarte, José Luis

doi:10.1016/j.scitotenv.2016.11.096

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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%

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Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Navares¹,

Aznarte²

2019

Atmosphere

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Navares¹,

Aznarte²

2019

Atmosphere

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“…Some previous studies have used neural networks (NNs) to estimate pollen. 9 – 13 In this article we use machine learning to explore the relative importance of a variety of environmental factors in estimating the airborne abundance of Ambrosia pollen over a 27-year period in Tulsa, OK.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to estimate atmosphericAmbrosiapollen concentrations in Tulsa, OK

Liu

Zewdie

et al. 2017

Environ�Health�Insights

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This article describes an example of using machine learning to estimate the abundance of airborne Ambrosia pollen for Tulsa, OK. Twenty-seven years of historical pollen observations were used. These pollen observations were combined with machine learning and a very complete meteorological and land surface context of 85 variables to estimate the daily Ambrosia abundance. The machine learning algorithms employed were Least Absolute Shrinkage and Selection Operator (LASSO), neural networks, and random forests. The best performance was obtained using random forests. The physical insights provided by the random forest are also discussed.

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Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid

Navares

Díaz

Linares

et al. 2018

Stoch Environ Res Risk Assess

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What are the most important variables for Poaceae airborne pollen forecasting?

Cited by 10 publications

References 30 publications

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Using machine learning to estimate atmosphericAmbrosiapollen concentrations in Tulsa, OK

Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid

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