[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120430-en":3,"doc-seo-120430-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},120430,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","PROVIDING WAVELENGTH RESOLVED IRRADIANCE MEASUREMENTS BY USING MACHINE LEARNING - Dissertation","Sunlight incident on Earth’s atmosphere drives atmospheric photo-chemistry, which underpins urban air quality understanding and related human health impacts. This dissertation addresses the lack of real-time wavelength-resolved solar irradiance data across cities through two machine-learning solutions: calibrating low-cost sensors for neighborhood-scale measurements and estimating wavelength-resolved irradiance from solar zenith angle, Earth distance, and publicly available environmental datasets, avoiding additional sensor deployment.","PROVIDING WAVELENGTH RESOLVED IRRADIANCE MEASUREMENTS BY USING  \nMACHINE LEARNING  \nby  \nYichao Zhang  \nAPPROVED BY SUPERVISORY COMMITTEE:  \n\n| David J. Lary, Chair |\n| --- |\n| Phillip C. Anderson |\n| Xinchou Lou |\n| Fabiano S. Rodrigues |\n\nRussell Stoneback  \nCopyright © 2021 Yichao Zhang All rights reserved  \nDedicated to my handsome father, Chef Sanlian Zhang, my beautiful mother, Master Xiu Ye, my loving partner, Double Master Jun Zhou, and my one-year-old daughter, Hash  \nPROVIDING WAVELENGTH RESOLVED IRRADIANCE MEASUREMENTS BY USING  \nMACHINE LEARNING  \nby  \nYICHAO ZHANG, BS, MS  \nDISSERTATION  \nPresented to the Faculty of  \nThe University of Texas at Dallas  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDOCTOR OF PHILOSOPHY IN  \nPHYSICS  \nTHE UNIVERSITY OF TEXAS AT DALLAS  \nDecember 2021  \nACKNOWLEDGMENTS  \nThanks to my enthusiastic and helpful supervisor, Prof. David Lary. This British gentleman’s big smile has a magic power from Hogwarts, and always encouraged me in whatever situation. His valuable experience and amazing insight have significantly broadened our horizons and led us to the new world of data science and Piada restaurant.  \nThanks for the time and e↵ort of the professors on my academic committee. Particularly, thanks to Prof. Phillip Anderson for being my academic advisor for 6 years. Thanks to Prof. Xinchou Lou for giving me the first lesson of machine learning. Thanks to Prof. Fabiano Rodrigues for the helpful suggestions on my dissertation writing. Thanks to Prof. Russell Stoneback for attending my defense after leaving our university.  \nThanks to all my friendly colleagues. Specifically, thanks to Lakitha Wijeratne for the implementation of multiple sensors. Thanks to Shawhin Talebi for his code for collecting data from Minolta sensor. Thanks to Xiaohe Yu for sharing his experience and suggestions on data preprocessing. Thanks to Prof. Lary’s wife for collecting the street-level data. Thanks to Prof. Lary’s son for the design of the holder for the auto shutter.  \nI would like to thank all the professors who gave me courses at UTD. And thanks to UTD Iron Man, Mr. David Taylor, for helping me on many devices, which were used in my teaching and research.  \nSince I survived the COVID-19 pandemic, thanks to Pfizer for the mRNA vaccines.  \nAt last, I would like to thank my parents for the love and support from the other side of the world. With their financial support, I bought my first car, which carried me for more than 5 years. Thank you to my dear partner Jun Zhou for the long-term company and support. Thank the cutest kitten, Hash, for being a member of our family.  \nSeptember 2021  \nPROVIDING WAVELENGTH RESOLVED IRRADIANCE MEASUREMENTS BY USING  \nMACHINE LEARNING  \nYichao Zhang, PhD  \nThe University of Texas at Dallas, 2021  \nSupervising Professor: David J. Lary, Chair  \nSunlight incident on the Earth’s atmosphere is essential for life and is the driving force for atmospheric photo-chemistry. Atmospheric photo-chemistry is central to understanding urban air quality and the host of associated human health impacts. In this dissertation, two solutions were proposed to address the current lack of real-time wavelength-resolved solar irradiance data across cities.  \nOur first solution is based on the machine learning calibration of low-cost light sensors. These calibrated sensors have a strong performance and can be readily deployed at scale across dense urban environments to measure the wavelength resolved irradiance on a neighborhood scale. This work has been published in MDPI (Zhang et al., 2021) .  \nOur second solution is based on the comprehensive dataset from public environmental sensors. We developed another machine learning model to estimate the wavelength resolved solar irradiance from solar zenith angle, earth distance, and multiple environmental dataset, such as relative humidity, total column ozone, earth surface reflectance, and radar reflectivities in the sky. All these factors can ","cbCaigkGbmTMJPWa","https://ap.wps.com/l/cbCaigkGbmTMJPWa","pdf",31167917,1,126,"English","en",105,"# Acknowledgments\n# Dissertation Overview\n# Table of Contents\n## Chapter 1 Dissertation Goals\n## 1.1 Dissertation Goal 1\n## 1.2 Dissertation Goal 2\n## 1.3 Achieving These Goals\n## Chapter 2 Introduction\n## 2.1 Background\n## 2.2 Solar Irradiance\n## 2.3 Atmospheric Absorption and Scattering\n## 2.4 Changes in the Surface Solar Irradiance\n## Chapter 3 Remote Sensing\n## 3.1 Active and Passive Remote Sensing","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It tackles the current lack of real-time wavelength-resolved solar irradiance data across cities, which limits understanding of atmospheric photo-chemistry and downstream air-quality and health impacts.\"},{\"question\":\"What is the first proposed solution?\",\"answer\":\"It uses machine learning calibration of low-cost light sensors, enabling strong performance and scalable deployment in dense urban environments for neighborhood-scale wavelength-resolved irradiance measurement.\"},{\"question\":\"How does the second solution estimate wavelength-resolved irradiance without extra sensors?\",\"answer\":\"It builds a machine learning model using factors such as solar zenith angle, Earth distance, and multiple public environmental datasets (e.g., humidity, ozone, surface reflectance, radar reflectivities) to estimate irradiance at neighborhood scale.\"}]","PROVIDING WAVELENGTH RESOLVED IRRADIANCE MEASUREMENTS BY USING MACHINE LEARNING - 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