Assessment of neural networks and time series analysis to forecast airborne Parietaria pollen presence in the Atlantic coastal regions

Valencia, Jaime; Astray, Gonzalo; Fernández-González, María; Aira, María Jesús; Rodríguez-Rajo, F. J.

doi:10.1007/s00484-019-01688-z

Cited by 16 publications

(13 citation statements)

References 52 publications

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“…Scientific works examining the diurnal pollen variation in the air only seldom apply deterministic predictive models, narrowing their efforts down to descriptive methods and correlation analysis (Chappuis et al 2020;Fernández-Rodríguez et al 2014;Š čevková et al 2015). The most common predictive techniques used so far are linear or nonlinear regressions, with significant steps having been made the last few years (Nowosad et al 2018;Piotrowska, 2012;Ritenberga et al 2016), and timeseries analysis, based on Box-Jenkins methods (García-Mozo et al 2014;Ocana-Peinado et al 2008;Valencia et al 2019). Also, variables like meteorological factors are frequently considered, as they have been proven as significant predictors of airborne pollen concentrations.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, novel and more sophisticated forecasting techniques are starting to be employed, as in the case of machine learning, which is increasingly gaining scientific interest. Several aerobiological studies have implemented machine learning algorithms, at various scales of analysis, such as artificial neural networks (Iglesias-Otero et al 2015;Puc, 2012;Valencia et al 2019), random forests (Nowosad et al 2018;Zewdie et al 2019), and support vector machines (Zewdie et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Betula and Poaceae airborne pollen concentrations on a 3-hourly resolution in Augsburg, Germany: toward automatically generated, real-time predictions

et al. 2021

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Airborne allergenic pollen impact the health of a great part of the global population. Under climate change conditions, the abundance of airborne pollen has been rising dramatically and so is the effect on sensitized individuals. The first line of allergy management is allergen avoidance, which, to date, is by rule achieved via forecasting of daily pollen concentrations. The aim of this study was to elaborate on 3-hourly predictive models, one of the very few to the best of our knowledge, attempting to forecast pollen concentration based on near-real-time automatic pollen measurements. The study was conducted in Augsburg, Germany, during four years (2016–2019) focusing on Betula and Poaceae pollen, the most abundant and allergenic in temperate climates. ARIMA and dynamic regression models were employed, as well as machine learning techniques, viz. artificial neural networks and neural network autoregression models. Air temperature, relative humidity, precipitation, air pressure, sunshine duration, diffuse radiation, and wind speed were additionally considered for the development of the models. It was found that air temperature and precipitation were the most significant variables for the prediction of airborne pollen concentrations. At such fine temporal resolution, our forecasting models performed well showing their ability to explain most of the variability of pollen concentrations for both taxa. However, predictive power of Betula forecasting model was higher achieving R2 up to 0.62, whereas Poaceae up to 0.55. Neural autoregression was superior in forecasting Betula pollen concentrations, whereas, for Poaceae, seasonal ARIMA performed best. The good performance of seasonal ARIMA in describing variability of pollen concentrations of both examined taxa suggests an important role of plants’ phenology in observed pollen abundance. The present study provides novel insight on per-hour forecasts to be used in real-time mobile apps by pollen allergic patients. Despite the huge need for real-time, short-term predictions for everyday clinical practice, extreme weather events, like in the year 2019 in our case, still comprise an obstacle toward highly performing forecasts at such fine timescales, highlighting that there is still a way to go to this direction.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Betula and Poaceae airborne pollen concentrations on a 3-hourly resolution in Augsburg, Germany: toward automatically generated, real-time predictions

et al. 2021

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“…In recent years, Artificial Neural Networks have become particularly popular in modelling the non-linear behaviour of pollen (Astray et al 2016). ANNs are composed of individual neurons in multiple layers, mimicking the biological neural system and is able to learn from complex, noisy, incomplete data to model non-linear relationships (Valencia et al 2019).…”

Section: )mentioning

confidence: 99%

Recent developments in monitoring and modelling airborne pollen, a review

Maya‐Manzano

Smith

Markey

et al. 2020

Grana

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“…Due to the growing global problem of increasing pollen allergenicity, wide varieties of studies have addressed the prediction of allergic risk periods caused by high pollen concentrations [36][37][38][39][40][41][42]. In such situations, it is important to find a set of variables that enable the early prediction of pollen counts through the application of intelligent models [43].…”

Section: Introductionmentioning

confidence: 99%

Alnus Airborne Pollen Trends during the Last 26 Years for Improving Machine Learning-Based Forecasting Methods

Novo-Lourés

Fernández-González

Pavón

et al. 2023

Forests

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Black alder (Alnus glutinosa (L.) Gaertn.) is a species of tree widespread along Europe and belongs to mixed hardwood forests. In urban environments, the tree is usually located along watercourses, as is the case in the city of Ourense. This taxon belongs to the betulaceae family, so it has a high allergenic potential in sensitive people. Due to the high allergenic capacity of this pollen type and the increase in global temperature produced by climate change, which induces a greater allergenicity, the present study proposes the implementation of a Machine Learning (ML) model capable of accurately predicting high-risk periods for allergies among sensitive people. The study was carried out in the city of Ourense for 28 years and pollen data were collected by means of the Hirst trap model Lanzoni VPPS-2000. During the same period, meteorological data were obtained from the meteorological station of METEOGALICIA in Ourense. We observed that Alnus airborne pollen was present in the study area during winter months, mainly in January and February. We found statistically significant trends for the end of the main pollen season with a lag trend of 0.68 days per year, and an increase in the annual pollen integral of 112 pollen grains per year and approximately 12 pollen grains/m3 per year during the pollen peak. A Spearman correlation test was carried out in order to select the variables for the ML model. The best ML model was Random Forest, which was able to detect those days with medium and high labels.

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Assessment of neural networks and time series analysis to forecast airborne Parietaria pollen presence in the Atlantic coastal regions

Cited by 16 publications

References 52 publications

Forecasting Betula and Poaceae airborne pollen concentrations on a 3-hourly resolution in Augsburg, Germany: toward automatically generated, real-time predictions

Forecasting Betula and Poaceae airborne pollen concentrations on a 3-hourly resolution in Augsburg, Germany: toward automatically generated, real-time predictions

Recent developments in monitoring and modelling airborne pollen, a review

Alnus Airborne Pollen Trends during the Last 26 Years for Improving Machine Learning-Based Forecasting Methods

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