Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Abstract

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.

Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

“…Over the past few years, several studies using machinelearning algorithms have been conducted to monitoring of the airborne pollen or to develop automatic classification system of pollen grains [19], [20], [21]. There are also many studies that have utilized CNN models to classify various pollen species [22], [23], [24], [25], [26], [27]. However, our research is focused on establishing a reliable method for assessing pollen viability using germination images rather than identifying the pollen species covered in the palynology fields.…”