[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126592-en":3,"doc-seo-126592-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126592,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Study and Prediction of Air Quality in Smart Cities - Through Machine Learning Techniques - Considering Spatiotemporal Components - Doctoral Thesis","Air quality is a central concern for science, government, and society because elevated pollutant concentrations above defined thresholds can lead to severe health outcomes, including heart disease, stroke, chronic obstructive pulmonary disease, and lung cancer. Existing approaches struggle with the complexity arising from dependencies across both time and space. This dissertation presents machine learning and deep learning methods to capture multidimensional and complex spatiotemporal dependencies governing air quality formation. Key contributions include a meta-review of air quality prediction methods, data-driven modeling using air quality, meteorological, and traffic inputs for Madrid, and exploratory analysis to uncover interconnections and influential features for forecasting.","Study and Prediction of Air Quality in Smart Cities through Machine Learning Techniques Considering Spatiotemporal Components  \nDoctoral Thesis  \nDitsuhi Iskandaryan  \nSupervisors: Dr. Francisco Ramos  \nDr. Sergio Trilles  \nA dissertation presented for the degree of Doctor of Computer Science  \nCastell de la Plana (Spain)  \nFebruary 2023  \nPrograma de Doctorado en Informtica Escuela de Doctorado de la Universitat Jaume I  \nESTUDIO Y PREDICCI´ON DE LA CALIDAD DEL AIRE EN CIUDADES INTELIGENTES MEDIANTE T ´ECNICAS DEAPRENDIZAJE AUTOM´ATICO CONSIDERANDO COMPONENTES ESPACIOTEMPORALES  \nMemoria presentada por Ditsuhi Iskandaryan para optar al grado de doctor  \npor la Universitat Jaume I  \nAutor Directores  \nDitsuhi Iskandaryan Dr. Francisco Ramos y Dr. Sergio Trilles  \nDITSUHI|  \nISKANDARYAN  \nDigitally signed by DITSUHI|ISKANDARYAN Date: 2023.02.17  \n10:29:51 +01'00'  \nJOSE  \nFRANCISCO|RAMOS| ROMERO  \nDigitally signed by JOSE FRANCISCO| RAMOS|ROMERO Date: 2023.02.17  \n12:04:43 +01'00'  \nSERGIO| TRILLES| OLIVER  \nFirmado digitalmente por SERGIO|TRILLES| OLIVER  \nFecha: 2023.02.17  \n10:32:02 +01'00'  \nCastell de la Plana (Spain) Febrero 2023  \nFinancial Support  \nThis thesis has been realised with the financial support of the following institutions:  \nPredoctoral contract:  \n• Ayuda predoctoral para la formacin de personal investigador FPI-UJI, dentro del Plan de Promocin de la Investigacin de la UJI 2018 (Ref. PREDOC/2018/61) . Universitat Jaume I. 1st September 2019 - 9th December 2022.  \nResearch stay:  \n• University of Bologna, Bologna, Italy (1st September 2021-31st December 2021) . Financed by Beca para realizar estancias temporales en otros centros de investigacin, para el personal docente e investigador de la Universidad del Plan de Promiocin de la Investigacin de la UJI 2020 (Ref. E-2020-14) .  \nExternal fundings:  \n• ValidanT project (GV/2020/035 Sergio Trilles) . 1st January 2020-31st December 2021 . Funded by Generalitat Valenciana.  \n• Trust4IoE project (PID2019-104065GA-I00) from the Spanish Ministry of Science and Innovation. 1st June 2020 - 31st May 2023 . Funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by MCIN/AEI/10.13 039/501100011033 and by “ERDF, a way of making Europe”, by the European Union.  \nStudy and Prediction of Air Quality in Smart Cities through Machine Learning Techniques Considering Spatiotemporal Components. Copyright © 2023 Ditsuhi Iskandaryan. This work is licensed under CC Attribution-ShareAlike (BY-SA) .  \niii  \nAcknowledgments  \nI would like to acknowledge the predoctoral programme PINV2018-Universitat Jaume I (PREDOC/2018/61) and the Pla de promoci de la investigaci a l’UJI (E-2020-14) for their financial support. I am very thankful for the opportunity to pursue my PhD, as well as to carry out my research stay at the University of Bologna.  \nThis journey would not have been possible without the support and encouragement of my supervisors Dr. Francisco Ramos and Dr. Sergio Trilles. I am extremely grateful for their patience, motivation, constructive feedback and guidance that led me to accomplish this dissertation and become an independent researcher.  \nI would like to express my deepest gratitude to Dr. Joaqu ´ın Huerta for his continued support, encouragement and willingness to resolve any issues that arise.  \nSpecial thanks to Estefania Aguilar for her assistance during her tenure at GEOTEC, especially her endless patience and support during the PhD application and post-admission processes.  \nMy sincere appreciation goes out to Prof. Silvana Di Sabatino and her research group, especially Dr. Erika Brattich, Dr. Francesca Di Nicola and Dr. Leonardo Arago, for their immense knowledge, insightful comments, and encouragement throughout my stay in Bologna.  \nAlso, I would like to thank the staff and all the colleagues in the GEOTEC research group for the amazing working environment and the interesting discussions.  \nBig thanks to my friends for their encouragement, invaluable advice a","cbCaikzOys93R9RC","https://ap.wps.com/l/cbCaikzOys93R9RC","pdf",10090624,1,197,"English","en",105,"# Financial Support\n# Acknowledgments\n# Abstract\n## Problem background and motivation\n## Proposed approach and contributions","[{\"question\":\"Why is air quality prediction important according to the dissertation abstract?\",\"answer\":\"Because pollutant concentrations above thresholds can cause serious diseases. Information about air quality supports monitoring and control to reduce harmful impacts.\"},{\"question\":\"What main technical direction does the dissertation propose?\",\"answer\":\"Machine learning and deep learning techniques that process multidimensional information and model complex spatiotemporal dependencies related to air quality formation.\"},{\"question\":\"What are the dissertation’s key contributions highlighted in the abstract?\",\"answer\":\"A meta-review of state-of-the-art prediction approaches, incorporation of air quality, meteorological, and traffic data in spatiotemporal dimensions for Madrid, and exploratory dataset analysis to identify interconnections and forecasting-relevant features.\"}]","Study and Prediction of Air Quality in Smart Cities - Through Machine Learning Techniques - Considering Spatiotemporal Components - Doctoral Thesis | PDF",1785933567,496,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"study-and-prediction-of-air-quality-in-smart-cities-through-machine-learning-techniques-considering-spatiotemporal-components-doctoral-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/study-and-prediction-of-air-quality-in-smart-cities-through-machine-learning-techniques-considering-spatiotemporal-components-doctoral-thesis/126592/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is air quality prediction important according to the dissertation abstract?","Question",{"text":76,"@type":77},"Because pollutant concentrations above thresholds can cause serious diseases. 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