[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124263-en":3,"doc-seo-124263-105":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":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},124263,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning for Urban Water Runoff Prediction and Water Temperature Forecasting - PhD Dissertation","This dissertation applies machine learning (ML) to two hydrology problems: urban stormwater runoff prediction and water temperature forecasting at Tennessee Valley Authority fossil plants. Data-driven models are built to approximate outputs from the Stormwater Management Model (SWMM), reducing computational cost while maintaining predictive accuracy. A First Creek watershed case study uses rainfall and subcatchment characteristics with feature engineering and clustering to compare model outputs. Additional chapters analyze temperature trends and forecast hourly temperatures to evaluate environmental impacts.","University of Tennessee, Knoxville  \nTRACE: Tennessee Research and Creative Exchange  \n\n| Doctoral Dissertations | Graduate School |\n| --- | --- |\n| 5-2025\u003Cbr>Machine Learning for Urban Water Runoff Prediction and Water Temperature Forecasting\u003Cbr>Rachel Wood-Ponce\u003Cbr>University of Tennessee, Knoxville, [rwood25@vols.utk.edu](rwood25@vols.utk.edu)\u003Cbr>Follow this and additional works at: [https://trace.tennessee.edu/utk_graddiss](https://trace.tennessee.edu/utk_graddiss)\u003Cbr> Part of the Environmental Engineering Commons, and the Industrial Engineering Commons |  |\n\nRecommended Citation  \nWood-Ponce, Rachel, \"Machine Learning for Urban Water Runoff Prediction and Water Temperature Forecasting. \" PhD diss., University of Tennessee, 2025.  \n[https://trace.tennessee.edu/utk_graddiss/12324](https://trace.tennessee.edu/utk_graddiss/12324)  \nThis Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [trace@utk.edu](trace@utk.edu).  \nTo the Graduate Council:  \nI am submitting herewith a dissertation written by Rachel Wood-Ponce entitled \"Machine Learning for Urban Water Runoff Prediction and Water Temperature Forecasting.\" I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Industrial Engineering.  \nAnahita Khojandi, Major Professor  \nWe have read this dissertation and recommend its acceptance: Jon Hathaway, Bing Yao, Hugh Medal  \nAccepted for the Council: Dr. Amy Cathey  \nVice Provost and Dean of the Graduate School  \n(Original signatures are on file with official student records.)  \nTo the Graduate Council:  \nI am submitting herewith a dissertation written by Rachel Wood-Ponce entitled“Machine Learning for Urban Water Runoff Prediction and Water Temperature Forecasting.” I have examined the final paper copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Industrial and Systems Engineering.  \nAnahita Khojandi, Major Professor  \nWe have read this dissertation and recommend its acceptance:  \n\n| Jon Hathaway |\n| --- |\n| Hugh Medal |\n\nBing Yao  \nAccepted for the Council:  \nDixie L. Thompson  \nVice Provost and Dean of the Graduate School  \nTo the Graduate Council:  \nI am submitting herewith a dissertation written by Rachel Wood-Ponce entitled“Machine Learning for Urban Water Runoff Prediction and Water Temperature Forecasting.” I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Industrial and Systems Engineering.  \nAnahita Khojandi, Major Professor  \nWe have read this dissertation and recommend its acceptance:  \nJon Hathaway  \n\n| Hugh Medal |\n| --- |\n| Bing Yao |\n\nAccepted for the Council: Dixie L. Thompson  \nVice Provost and Dean of the Graduate School  \n(Original signatures are on file with official student records.)  \nMachine Learning for Urban Water Runoff Prediction and Water Temperature Forecasting  \nA Dissertation Presented for the Doctor of Philosophy  \nDegree  \nThe University of Tennessee, Knoxville  \nRachel Wood-Ponce  \nMay 2025  \n© by Rachel Wood-Ponce, 2025 All Rights Reserved.  \nii  \nThis dissertation is dedicated to my daughter, Alejandra, my husband, Moises, my parents, Sean and Elizabeth, and my sisters, Bethany, Shawna, and Sarah for their love and belief in me for the last 4 and a half years.  \nAcknowledgements  \nI extend my gratitude to my advisor, Dr. Khojandi, and the members of my committee for their support througho","cbCaicLTAJvxfUki","https://ap.wps.com/l/cbCaicLTAJvxfUki","pdf",3966218,1,118,"English","en",105,"# Abstract\n## Chapter 1: Urban stormwater runoff prediction\n## Chapter 2: Water temperature trends and correlations\n## Chapter 3: Hourly water temperature forecasting and impacts","[{\"question\":\"What is the core research focus of the dissertation?\",\"answer\":\"The dissertation investigates how machine learning methods can support hydrology tasks, specifically urban stormwater runoff prediction and water temperature forecasting at TVA fossil plants.\"},{\"question\":\"How does Chapter 1 reduce computational burden in runoff prediction?\",\"answer\":\"Chapter 1 develops ML models to approximate SWMM outputs. Because SWMM is computationally intensive, the ML approach speeds up runoff predictions while preserving accuracy.\"},{\"question\":\"What do later chapters target regarding water temperatures?\",\"answer\":\"Chapter 2 examines trends, fluctuations, and correlations in water temperature data. Chapter 3 forecasts hourly temperatures to assess efficiency considerations and potential impacts on aquatic life.\"}]","Machine Learning for Urban Water Runoff Prediction and Water Temperature Forecasting - PhD Dissertation | PDF",1785821273,297,{"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},"machine-learning-for-urban-water-runoff-prediction-and-water-temperature-forecasting-phd-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/machine-learning-for-urban-water-runoff-prediction-and-water-temperature-forecasting-phd-dissertation/124263/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the core research focus of the dissertation?","Question",{"text":75,"@type":76},"The dissertation investigates how machine learning methods can support hydrology tasks, specifically urban stormwater runoff prediction and water temperature forecasting at TVA fossil plants.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Chapter 1 reduce computational burden in runoff prediction?",{"text":80,"@type":76},"Chapter 1 develops ML models to approximate SWMM outputs. 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