[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121281-en":3,"doc-seo-121281-105":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},121281,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","PHYSICAL SENSING AND PHYSICS-BASED MACHINE LEARNING FOR ACTIONABLE ENVIRONMENTAL INSIGHTS - Dissertation","Current methods for evaluating water quality rely on sparse reference measurements and infrequent satellite observations, while air quality standards are often based on annual and 24-hour averages that overlook short-term concentration spikes. This dissertation develops physics-based machine learning methods to close these measurement gaps through an autonomous system combining drone hyperspectral imaging with collocated in situ sensing. Models map reflectance spectra to water quality parameters, support unsupervised source identification, and extend Koopman-based time-series modeling for real-time particulate matter forecasting.","PHYSICAL SENSING AND PHYSICS-BASED MACHINE LEARNING FOR ACTIONABLE ENVIRONMENTAL INSIGHTS  \nby  \nJohn Waczak  \nAPPROVED BY SUPERVISORY COMMITTEE:  \n\n| David J. Lary, Chair |\n| --- |\n| Christopher Simmons |\n| David Lumley |\n| Lindsay J. King |\n\nJoseph M. Izen  \nCopyright © 2024 John Waczak  \nAll rights reserved  \nPHYSICAL SENSING AND PHYSICS-BASED MACHINE LEARNING FOR ACTIONABLE ENVIRONMENTAL INSIGHTS  \nby  \nJOHN WACZAK, BS  \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 2024  \nACKNOWLEDGMENTS  \nCompleting this work would not have been possible without the help and support of many people in my life. Simply put: it took a village. First and foremost, I extend my gratitude tomy supervisor, Dr. David Lary for his wisdom, inspiration, and guidance across our various research projects. I could not have asked for a better mentor. I also extend my thanks to my committee members Dr. Christopher Simmons, Dr. David Lumley, Dr. Lindsay King, and Dr. Joseph Izen for their valuable insights and feedback.  \nNext, I’d like to acknowledge my colleagues in the MINTS research group. Major thanks goto Adam Aker, Shawhin Talebi, Ashen Fernando, and Lakitha Weijeratne for the insightful discussions during our group meetings and the feedback they provided on my research manuscripts. Additionally, I thank David Schaefer, Prabuddha Dewage, Mazhar Iqbal, Gokul Balagopal, Matthew Lary, and Tatiana Lary for their assistance during our robot team field deployments.  \nLast, none of this would have been possible without the abundance of support from my family and friends. Their persistent encouragement kept me sane across three moves, a global pandemic, and countless late-night debugging sessions.  \nOctober 2024  \nPHYSICAL SENSING AND PHYSICS-BASED MACHINE LEARNING FOR  \nACTIONABLE ENVIRONMENTAL INSIGHTS  \nJohn Waczak, PhD  \nThe University of Texas at Dallas, 2024  \nSupervising Professor: David J. Lary, Chair  \nCurrent methods for the evaluation of water quality are limited by sparse reference measurements and infrequent satellite observations. Meanwhile, air quality standards are assessed at annual and 24-hour averages which neglect the impact of short-term spikes on local pollution exposure. This dissertation develops physics-based machine learning methods to fill these gaps. To significantly accelerate water quality assessment, we design an autonomous robotic team combining drone-based hyperspectral imaging with collocated, in situ data collection by an autonomous boat. Models are trained to map observed reflectance spectra into 13 physical, chemical, ionic, and biochemical water quality parameters. These models are then deployed to map the small-scale spatial variability of water quality across a North Texas pond. For scenarios in which specific contaminants are not known in advance, we utilize unsupervised machine learning to visualize the distribution of water-leaving reflectance spectra and identify spectral signatures corresponding to unique sources. As a key innovation, we extend this approach by introducing a novel machine learning method called Generative Simplex Mapping for nonlinear spectral unmixing. Using real data from a rhodamine tracer dye release, we demonstrate the ability of this model to successfully identify localized contaminant sources. Finally, we leverage data from a distributed network of low-cost air quality monitors to construct time series models for real time particulate matter measurements. The approach  \nextends the Hankel Alternative View of Koopman framework to identify acute pollution spikes and enable multi-step forecasts.  \nTABLE OF CONTENTS  \nACKNOWLEDGMENTS ................................. iv  \nABSTRACT ........................................ v  \nLIST OF FIGURES .................................... xi  \nLIST OF TABLES .................","cbCaimHXeQ7AA1za","https://ap.wps.com/l/cbCaimHXeQ7AA1za","pdf",57090778,1,232,"English","en",105,"# Acknowledgments\n# Abstract\n# List of Figures\n# List of Tables\n# Chapter 1 Introduction\n## Water Quality\n## Air Quality\n## Dissertation Goals\n## Research Contributions\n## Dissertation Overview\n# Chapter 2 A Coordinated Team of Autonomous Robots for the Rapid Assessment of Water Quality\n## Light and Water\n## Coordinated Robot Teams\n## Rapid Processing and Georectification of Hyperspectral Data Cubes\n# Chapter 3 Distributed Sensor Networks for Real-Time Air Quality Assessment\n## Measurement of Particulate Matter\n## A Low Cost Sensor Network For Air Quality Monitoring\n# Chapter 4 Characterizing Water Quality with Autonomous Robotic Teams and Supervised Learning\n## Motivation\n## Study Overview\n## Supervised Machine Learning\n## Modeling Approach\n## Results","[{\"question\":\"Why do existing water and air quality evaluation methods fall short?\",\"answer\":\"Water quality assessments are limited by sparse reference sampling and infrequent satellite observations, while air quality standards frequently rely on annual and 24-hour averages that miss short-term spikes affecting local exposure.\"},{\"question\":\"How does the dissertation accelerate water quality assessment in practice?\",\"answer\":\"It uses an autonomous robotic team that combines drone-based hyperspectral imaging with collocated in situ measurements from an autonomous boat, then trains models to map reflectance spectra to multiple water quality parameters.\"},{\"question\":\"What is the role of unsupervised learning in this work?\",\"answer\":\"When specific contaminants are unknown, unsupervised machine learning visualizes the distribution of water-leaving reflectance spectra and identifies spectral signatures associated with distinct sources.\"}]","PHYSICAL SENSING AND PHYSICS-BASED MACHINE LEARNING FOR ACTIONABLE ENVIRONMENTAL INSIGHTS - Dissertation | PDF",1785734893,585,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"physical-sensing-and-physics-based-machine-learning-for-actionable-environmental-insights-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@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/physical-sensing-and-physics-based-machine-learning-for-actionable-environmental-insights-dissertation/121281/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do existing water and air quality evaluation methods fall short?","Question",{"text":75,"@type":76},"Water quality assessments are limited by sparse reference sampling and infrequent satellite observations, while air quality standards frequently rely on annual and 24-hour averages that miss short-term spikes affecting local exposure.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation accelerate water quality assessment in practice?",{"text":80,"@type":76},"It uses an autonomous robotic team that combines drone-based hyperspectral imaging with collocated in situ measurements from an autonomous boat, then trains models to map reflectance spectra to multiple water quality parameters.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the role of unsupervised learning in this work?",{"text":84,"@type":76},"When specific contaminants are unknown, unsupervised machine learning visualizes the distribution of water-leaving reflectance spectra and identifies spectral signatures associated with distinct sources.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]