Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece

Voukantsis, Dimitris; Niska, Harri; Karatzas, Kostas; Riga, Marina; Damialis, Athanasios; Vokou, Despoina

doi:10.1016/j.atmosenv.2010.09.006

Cited by 55 publications

(34 citation statements)

References 31 publications

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“…For Szeged, MLP provides the best results for the forecasting horizon (1-7 days) that is confirmed by former studies (Sánchez-Mesa et al, 2002;Voukantsis et al, 2010). 1-day forecast indicates the best performance.…”

Section: Performance Of the Forecasting Modelssupporting

confidence: 77%

“…These capabilities have already been successfully utilized in previous studies in order to forecast pollen concentrations (e.g. Voukantsis et al, 2010), therefore MLP is an important procedure and this is the first occasion ).…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

“…When forecasting, the following values of coefficient of determination (R 2 ) (i.e. squared correlations) of one day ahead forecasts were received: 0.60 for Poaceae using neural networks (Sánchez-Mesa et al, 2002); 0.93 again for Poaceae using neural networks (Rodríguez-Rajo et al, 2010); 0.45 for grass pollen (whole season) using correlation analysis (Stach et al, 2008) and 0.79 for Poaceae using Multiple Linear Regression (Voukantsis et al, 2010). Our study provides a coefficient of determination of 0.98 (Ambrosia, Szeged, one day and two days ahead) using Multi-Layer Perceptron that ranks this model the best one in the literature.…”

Section: Performance Of the Forecasting Modelsmentioning

confidence: 99%

“…However, methods of Computational Intelligence (CI) have only been scarcely applied in airborne pollen related studies. They were used for forecasting (a) daily pollen concentrations (Delaunay et al, 2004, cedar pollen;Aznarte et al, 2007, olive pollen;Rodríguez-Rajo et al, 2010, Poaceae pollen;Voukantsis et al, 2010, Oleaceae, Poaceae and Urticaceae pollen; Puc, 2012; Betula pollen), (b) pollen-induced symptoms (Voukantsis et al, 2013), (c) risk level of Betula pollen in the air (CastellanoMéndez et al, 2005) and (d) the severity of the Poaceae pollen season (Sánchez Mesa et al, 2005). Furthermore, Aznarte et al (2007) used neuro-fuzzy models for forecasting olive pollen concentrations.…”

Section: Introductionmentioning

confidence: 99%

“…optimization and forecasting), 3) are computationally efficient and can be easily integrated into operational use of the models (Voukantsis et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Predicting daily ragweed pollen concentrations using Computational Intelligence techniques over two heavily polluted areas in Europe

Csépe

Makra

Voukantsis

et al. 2014

Science of The Total Environment

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• For Szeged, MLP and tree-based models, while for Lyon only MLP performs well for predicting pollen concentration.• When predicting alarm levels, the performance of MLP is the best for both cities.• When forecasting high pollen episodes, the more complex CI methods prove better for both cities.• The selection of the optimal method depends on climate, as a function of geographical location and relief. a b s t r a c t a r t i c l e i n f o Forecasting ragweed pollen concentration is a useful tool for sensitive people in order to prepare in time for high pollen episodes. The aim of the study is to use methods of Computational Intelligence (CI) (Multi-Layer Perceptron, M5P, REPTree, DecisionStump and MLPRegressor) for predicting daily values of Ambrosia pollen concentrations and alarm levels for 1-7 days ahead for Szeged (Hungary) and Lyon (France), respectively. Ten-year daily mean ragweed pollen data (within 1997-2006) are considered for both cities. 10 input variables are used in the models including pollen level or alarm level on the given day, furthermore the serial number of the given day of the year within the pollen season and altogether 8 meteorological variables. The study has novelties as (1) daily alarm thresholds are firstly predicted in the aerobiological literature; (2) data-driven modelling methods including neural networks have never been used in forecasting daily Ambrosia pollen concentration; (3) algorithm J48 has never been used in palynological forecasts; (4) we apply a rarely used technique, namely factor analysis with special transformation, to detect the importance of the influencing variables in defining the pollen levels for 1-7 days ahead. When predicting pollen concentrations, for Szeged Multi-Layer Perceptron models deliver similar results with tree-based models 1 and 2 days ahead; while for Lyon only Multi-Layer Perceptron provides acceptable result. When predicting alarm levels, the performance of Multi-Layer Perceptron is the best for both cities. It is presented that the selection of the optimal method depends on climate, as a function of geographical location and relief. The results show that the more complex CI methods perform well, and their performance is case-specific for ≥2 days forecasting horizon. A determination coefficient of 0.98 (Ambrosia, Szeged, one day and two days ahead) using Multi-Layer Perceptron ranks this model the best one in the literature.

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Section: Performance Of the Forecasting Modelssupporting

confidence: 77%

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Section: Performance Of the Forecasting Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…optimization and forecasting), 3) are computationally efficient and can be easily integrated into operational use of the models (Voukantsis et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting daily ragweed pollen concentrations using Computational Intelligence techniques over two heavily polluted areas in Europe

Csépe

Makra

Voukantsis

et al. 2014

Science of The Total Environment

Self Cite

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Machine Learning, Big Data, and Spatial Tools: A Combination to Reveal Complex Facts That Impact Environmental Health

Lary

Wijeratne

Zewdie

et al. 2021

Geospatial Technology for Human Well-Being and Health

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Earth Observation Open Science and Innovation

Mathieu¹,

Aubrecht²

2018

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Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece

Cited by 55 publications

References 31 publications

Predicting daily ragweed pollen concentrations using Computational Intelligence techniques over two heavily polluted areas in Europe

Predicting daily ragweed pollen concentrations using Computational Intelligence techniques over two heavily polluted areas in Europe

Machine Learning, Big Data, and Spatial Tools: A Combination to Reveal Complex Facts That Impact Environmental Health

Earth Observation Open Science and Innovation

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