e-Science workflow: A semantic approach for airborne pollen prediction

Allergic rhinitis has become a global health problem in recent decades because airborne pollen is a primary trigger of this respiratory disorder. Moreover, pollinosis can exacerbate the symptoms of asthma and favour respiratory infections. Seasonal pollen trends and climatic circumstances (such as temperature, precipitation, relative humidity, wind speed and direction, and other variables) can impact daily airborne pollen concentrations, influencing local pollen emission and dispersion. Because of that, pollen monitoring and prediction are becoming more relevant to the urban population and scientific interest is put into them. Due to such tasks' high volume of data, scientists are starting to use computational tools like workflows to automate and speed up the process. Furthermore, using the expert scientific domain is critical for improving the analysis, allowing, among others, a better workflow configuration and data provenance. As semantic web technologies have been revealed as an essential means for knowledge representation, we implemented this workflow information as an ontology using formats like RDF(S) and OWL. Consequently, this paper provides a semantic-enhanced e-Science workflow based on the TITAN framework for pollen forecasting analysis using meteorological data. Furthermore, a catalogue of components is developed on the TITAN framework, which allows the creation of different workflow versions. Two case studies of pollen prediction were developed to test the implementation of the aforementioned methodologies. Both were elaborated with airborne pollen data obtained in the city of Malaga (Spain). Still, one was elaborated for Platanus pollen type (narrow annual main pollination period), while the other was done for Amaranthaceae pollen type (extensive annual main pollination period). The predictions have been conducted using machine and deep learning algorithms like SARIMA or CNN-LSTM that intend to optimise the pollen prediction procedure depending on its stational and seasonal profile.