Applicability of Python-based geospatial analysis for operational assessment of weather station deployment

Sládek, David

doi:10.1109/icmt58149.2023.10171287

Cited by 2 publications

References 8 publications

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

Sládek,

Marková,

Talhofer

2024

Sustainability

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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.

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

Sládek,

Marková,

Talhofer

2024

Sustainability

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Python Programming for Geospatial Applications

Das,

Natarajan

2024

Advances in Geospatial Technologies

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Geospatial solutions represent a pivotal toolset for analyzing, interpreting, and visualizing spatial data across diverse domains, facilitating informed decision-making and fostering innovation. This book chapter provides a comprehensive overview of geospatial solutions, emphasizing their critical role in addressing spatially explicit challenges and driving efficiency, productivity, and innovation across various sectors. Furthermore, it explores the integration of Python programming in geospatial applications, highlighting its versatility and extensive ecosystem of libraries and tools tailored for spatial data analysis and visualization. The fundamentals of web mapping are discussed in depth, elucidating spatial representation, technologies, and tools commonly employed in web mapping applications. Also, the chapter explores Python's role in retrieving geospatial data with Python, visualization methods, and interactive web mapping.

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Applicability of Python-based geospatial analysis for operational assessment of weather station deployment

Cited by 2 publications

References 8 publications

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

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

Python Programming for Geospatial Applications

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