Enhancing weather analysis with data interpolation and time series forecasting

Weather analysis plays a crucial role in various domains, from agriculture to urban planning. However, accurate and localized predictions can be challenging in urban environments with limited weather station coverage. In this work, we propose a comprehensive workflow that combines dot rain coverage,...

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Detalles Bibliográficos
Autor: González Barberá, Alejandro
Tipo de recurso: tesis de maestría
Fecha de publicación:2023
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/148434
Acceso en línea:https://hdl.handle.net/10609/148434
Access Level:acceso abierto
Palabra clave:weather analysis
time series
machine learning
data interpolation
Machine learning -- TFM
Aprenentatge automàtic -- TFM
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spelling Enhancing weather analysis with data interpolation and time series forecastingGonzález Barberá, Alejandroweather analysistime seriesmachine learningdata interpolationMachine learning -- TFMAprenentatge automàtic -- TFMWeather analysis plays a crucial role in various domains, from agriculture to urban planning. However, accurate and localized predictions can be challenging in urban environments with limited weather station coverage. In this work, we propose a comprehensive workflow that combines dot rain coverage, interpolation, and machine learning models to address this issue. By establishing a network of weather stations strategically distributed across the city and utiliz- ing their weather variables as input for the interpolation techniques, we generate interpolated data for the entire city grid. This approach enables us to fill the gaps in weather station coverage and provide accurate predictions for locations without direct measurements. Subsequently, machine learning models are trained on the interpolated data to forecast var- ious weather variables. We conducted extensive experiments and hyperparameter optimization to achieve accurate predictions with low evaluation loss. Furthermore, our models demonstrate transferability across different weather stations within the city, enabling localized predictions in previously unmonitored areas. The results highlight the effectiveness of our project in im- proving weather analysis capabilities in urban settings. This work opens avenues for further research in applying these techniques to different regions with diverse weather conditions, ultimately enhancing decision-making processes in various sectors reliant on accurate weather predictions.Universitat Oberta de Catalunya (UOC)Solé-Ribalta, AlbertIserte, Sergio202320232023info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfapplication/vnd.openxmlformats-officedocument.presentationml.presentationhttps://hdl.handle.net/10609/148434reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésCC BY-NC-NDhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1484342026-05-28T12:42:01Z
dc.title.none.fl_str_mv Enhancing weather analysis with data interpolation and time series forecasting
title Enhancing weather analysis with data interpolation and time series forecasting
spellingShingle Enhancing weather analysis with data interpolation and time series forecasting
González Barberá, Alejandro
weather analysis
time series
machine learning
data interpolation
Machine learning -- TFM
Aprenentatge automàtic -- TFM
title_short Enhancing weather analysis with data interpolation and time series forecasting
title_full Enhancing weather analysis with data interpolation and time series forecasting
title_fullStr Enhancing weather analysis with data interpolation and time series forecasting
title_full_unstemmed Enhancing weather analysis with data interpolation and time series forecasting
title_sort Enhancing weather analysis with data interpolation and time series forecasting
dc.creator.none.fl_str_mv González Barberá, Alejandro
author González Barberá, Alejandro
author_facet González Barberá, Alejandro
author_role author
dc.contributor.none.fl_str_mv Solé-Ribalta, Albert
Iserte, Sergio
dc.subject.none.fl_str_mv weather analysis
time series
machine learning
data interpolation
Machine learning -- TFM
Aprenentatge automàtic -- TFM
topic weather analysis
time series
machine learning
data interpolation
Machine learning -- TFM
Aprenentatge automàtic -- TFM
description Weather analysis plays a crucial role in various domains, from agriculture to urban planning. However, accurate and localized predictions can be challenging in urban environments with limited weather station coverage. In this work, we propose a comprehensive workflow that combines dot rain coverage, interpolation, and machine learning models to address this issue. By establishing a network of weather stations strategically distributed across the city and utiliz- ing their weather variables as input for the interpolation techniques, we generate interpolated data for the entire city grid. This approach enables us to fill the gaps in weather station coverage and provide accurate predictions for locations without direct measurements. Subsequently, machine learning models are trained on the interpolated data to forecast var- ious weather variables. We conducted extensive experiments and hyperparameter optimization to achieve accurate predictions with low evaluation loss. Furthermore, our models demonstrate transferability across different weather stations within the city, enabling localized predictions in previously unmonitored areas. The results highlight the effectiveness of our project in im- proving weather analysis capabilities in urban settings. This work opens avenues for further research in applying these techniques to different regions with diverse weather conditions, ultimately enhancing decision-making processes in various sectors reliant on accurate weather predictions.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/10609/148434
url https://hdl.handle.net/10609/148434
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv CC BY-NC-ND
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC BY-NC-ND
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
application/vnd.openxmlformats-officedocument.presentationml.presentation
dc.publisher.none.fl_str_mv Universitat Oberta de Catalunya (UOC)
publisher.none.fl_str_mv Universitat Oberta de Catalunya (UOC)
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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