[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127476-en":3,"doc-seo-127476-105":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},127476,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Detecting Breaking Waves and Measuring Bore Speeds in Optical Surf Zone Imagery using Machine Learning - Thesis","A machine learning algorithm is developed to detect breaking waves in optical remote sensing data collected under visually diverse conditions along a kilometer-scale beach in Duck, NC. Bore speeds are estimated from the breaking-wave detections and evaluated against theoretical models using surveyed bathymetry. Bathymetry inversion from the derived bore speeds is analyzed, showing low but systematic bias in the surf zone. The qualitative inversion results indicate the method captures morphological change across the experiment, offering a robust, low-cost approach for measuring wave-breaking patterns and dynamics over large surf zones.","Detecting Breaking Waves and Measuring Bore Speeds in Optical Surf Zone Imagery using Machine Learning  \nMalcolm James LeClair  \nA thesis  \nsubmitted in partial fulfillment of the  \nrequirements for the degree of  \nMaster of Science in Civil Engineering  \nUniversity of Washington  \n2025  \nCommittee:  \nChristie Hegermiller  \nJim Thomson  \nKara Koetje  \nProgram Authorized to Offer Degree:  \nCivil and Environmental Engineering  \n©Copyright 2025 Malcolm James LeClair  \nUniversity of Washington  \nAbstract  \nDetecting Breaking Waves and Measuring Bore Speeds in Optical Surf Zone Imagery using  \nMachine Learning  \nMalcolm James LeClair  \nCo-Chairs of the Supervisory Committee:  \nChristie Hegermiller  \nJim Thomson  \nDepartment of Civil and Environmental Engineering  \nA machine learning algorithm is developed to detect breaking waves in optical remote sensing data collected under visually diverse conditions along a kilometer-scale beach in Duck, NC. Bore speeds are estimated from the breaking-wave detections and are compared with theoretical models using surveyed bathymetry. Bathymetry inversion from the derived bore speeds is then explored, revealing low but systematic bias within the surf zone. Despite this limitation, a qualitative analysis of the inverted bathymetry demonstrates that the method captures morphological change over the course of the experiment. This method shows promise as a robust, low-cost approach for measuring wave-breaking patterns and dynamics across large surf zones. The results highlight important considerations for the data resolution, quality, and processing needed to achieve robust measurements of breaking waves using optical remote sensing.  \nTABLE OF CONTENTS  \nPage  \nChapter 1: Introduction ................................ 1  \n1.1 Nearshore and Beach Structure ......................... 2  \n1.2 Surface Gravity Waves .............................. 3  \n1.3 Depth Limited Breaking ............................. 3  \n1.4 Wave Celerity ................................... 5  \n1.4.1 Non-Breaking Waves ........................... 5  \n1.4.2 Breaking Waves .............................. 5  \n1.5 Remote Sensing for Bathymetry Inversion ................... 6  \n1.5.1 History of Remote Sensing Methods ................... 6  \n1.5.2 Modern Tools ............................... 7  \n1.5.3 Time Exposure Imagery ......................... 8  \n1.5.4 Wavenumber-Frequency Depth Inversion ................ 8  \n1.5.5 Celerity Depth Inversion ......................... 9  \n1.6 Remote Sensing of Breaking Waves ....................... 9  \n1.6.1 Intensity Thresholding .......................... 10  \n1.6.2 Temporal Gradients ............................ 10  \n1.6.3 Machine Learning ............................. 11  \n1.7 Limitations of Nearshore Remote Sensing .................... 12  \nChapter 2: Methods ................................... 14  \n2.1 Observations .................................... 14  \n2.2 Detecting Breaking Waves ............................ 16  \n2.2.1 Dataset Curation ............................. 19  \n2.2.2 Machine Learning ............................. 21  \n2.3 Calculating Speeds ................................ 24  \nChapter 3: Results ................................... 26  \n3.1 Breaking Rates Rb ................................ 26  \n3.2 Breaker Index ................................... 28  \n3.3 Breaker Speeds .................................. 29  \n3.4 Speed Calculation Sensitivity and Bias ..................... 31  \nChapter 4: Discussion .................................. 35  \n4.1 Inverted Bathymetry ............................... 35  \n4.1.1 Patterns of Error in Inverted Bathymetry ................ 37  \n4.2 Performance .................................... 39  \n4.3 Practical Considerations ............................. 40  \n4.3.1 Designing the Machine Learning Task .................. 40  \n4.3.2 Lighting Impacts ............................. 41  \n4.3.3 Generalization ............................... 42  \nChapter 5: Conclu","cbCaiqDj5qXS5VMV","https://ap.wps.com/l/cbCaiqDj5qXS5VMV","pdf",48546711,1,73,"English","en",105,"# Introduction\n## Nearshore and Beach Structure\n## Surface Gravity Waves\n## Depth Limited Breaking\n## Wave Celerity\n## Remote Sensing for Bathymetry Inversion\n## Remote Sensing of Breaking Waves\n# Methods\n## Observations\n## Detecting Breaking Waves\n### Dataset Curation\n### Machine Learning\n## Calculating Speeds\n# Results\n## Breaking Rates Rb\n## Breaker Index\n## Breaker Speeds\n## Speed Calculation Sensitivity and Bias\n# Discussion\n## Inverted Bathymetry\n### Patterns of Error in Inverted Bathymetry\n## Performance\n## Practical Considerations\n### Designing the Machine Learning Task\n### Lighting Impacts\n### Generalization\n# Conclusion","[{\"question\":\"How does the thesis detect breaking waves in optical remote sensing imagery?\",\"answer\":\"It develops a machine learning algorithm trained to identify breaking-wave patterns in optical remote sensing data collected under visually diverse conditions.\"},{\"question\":\"How are bore speeds computed from the detected breaking waves?\",\"answer\":\"Bore speeds are estimated directly from the breaking-wave detections and then compared with theoretical models using surveyed bathymetry.\"},{\"question\":\"What do the bathymetry inversion results reveal about accuracy and surf-zone bias?\",\"answer\":\"Bathymetry inversion from derived bore speeds shows low but systematic bias within the surf zone, while qualitative analysis indicates the method can track morphological change during the experiment.\"}]","Detecting Breaking Waves and Measuring Bore Speeds in Optical Surf Zone Imagery using Machine Learning - Thesis | PDF",1785939164,184,{"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},"detecting-breaking-waves-and-measuring-bore-speeds-in-optical-surf-zone-imagery-using-machine-learning-thesis","",{"@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/detecting-breaking-waves-and-measuring-bore-speeds-in-optical-surf-zone-imagery-using-machine-learning-thesis/127476/",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","2026-08-05",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},"How does the thesis detect breaking waves in optical remote sensing imagery?","Question",{"text":75,"@type":76},"It develops a machine learning algorithm trained to identify breaking-wave patterns in optical remote sensing data collected under visually diverse conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are bore speeds computed from the detected breaking waves?",{"text":80,"@type":76},"Bore speeds are estimated directly from the breaking-wave detections and then compared with theoretical models using surveyed bathymetry.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the bathymetry inversion results reveal about accuracy and surf-zone bias?",{"text":84,"@type":76},"Bathymetry inversion from derived bore speeds shows low but systematic bias within the surf zone, while qualitative analysis indicates the method can track morphological change during the experiment.","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"]