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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F25%3A00563478" target="_blank" >RIV/60162694:G43__/25:00563478 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2071-1050/16/13/5713" target="_blank" >https://www.mdpi.com/2071-1050/16/13/5713</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/su16135713" target="_blank" >10.3390/su16135713</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sustainability

ISSN

2071-1050

e-ISSN

2071-1050

Svazek periodika

16

Číslo periodika v rámci svazku

13

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

22

Strana od-do

5713

Kód UT WoS článku

001269674600001

EID výsledku v databázi Scopus

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