Automated Quality Assessment of (Citizen) Weather Stations
Today we have access to a vast amount of weather, air quality, noise or radioactivity data collected by individual around the globe. This volunteered geographic information often contains data of uncertain and of heterogeneous quality, in particular when compared to official in-situ measurements. This limits their application, as rigorous, work-intensive data cleaning has to be performed, which reduces the amount of data and cannot be performed in real-time. In this paper, we propose dynamically learning the quality of individual sensors by optimizing a weighted Gaussian process regression using a genetic algorithm. We chose weather stations as our use case as these are the most common VGI measurements. The evaluation is done for the south-west of Germany in August 2016 with temperature data from the Wunderground network and the Deutsche Wetter Dienst (DWD), in total 1561 stations. Using a 10-fold cross-validation scheme based on the DWD ground truth, we can show significant improvements of the predicted sensor reading. In our experiment we were obtain a 12.5% improvement on the mean absolute error.