Towards Sustainable Urban Mobility: Leveraging Machine Learning Methods for QA of Meteorological Measurements in the Urban Area

Abstract

Non-professional measurement networks offer vast data sources within urban areas that could significantly contribute to urban environment mapping and improve weather prediction in the cities. However, their full potential remains unused due to uncertainties surrounding their positioning, measurement quality, and reliability. This study investigates the potential of machine learning (ML) methods serving as a parallel quality control system, using data from amateur and professional weather stations in Brno, Czech Republic. The research aims to establish a quality control framework for measurement accuracy and assess ML methods for measurement labelling. Utilizing global model data as its main feature, the study examines the effectiveness of ML models in predicting temperature and wind speed, highlighting the challenges and limitations of utilizing such data. Results indicate that while ML models can effectively predict temperature with minimal computational demands, predicting wind speed presents greater complexity due to the higher spatial variability. Hyperparameter tuning does not significantly influence model performance, with changes primarily driven by feature engineering. Despite the improved performance observed in certain models and stations, no model demonstrates superiority in capturing changes not readily apparent in the data. The proposed ensemble approach, coupled with a control ML classification model, offers a potential solution for assessing station quality and enhancing prediction accuracy. However, challenges remain in evaluating individual steps and addressing limitations such as the use of global models and basic feature encoding. Future research aims to apply these methods to larger datasets and automate the evaluation process for scalability and efficiency to enhance monitoring capabilities in urban areas.