[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118597-en":3,"doc-seo-118597-105":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},118597,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning-guided field site selection for river classification","Sufficient abundance and variety of field site sampling are essential for accurate reach-scale river classification across regional stream networks supporting scientific research and river management. Random site selection or limited access leaves stream characteristics underexplored, producing incomplete or inaccurate classifications. This work proposes a practical, general field site selection framework that integrates machine learning with human-in-the-loop iteration. It combines machine-learning-based initial selection from prior datasets, accessibility and observation checks, and iterative decision refinement using uncertainty information. Applied to the San Francisco Bay Area, the framework derives representative known and newly identified stream types, replaces inaccessible sites, and uses field surveys to validate differences between high-certainty and high-uncertainty sites.","UC Davis  \nUC Davis Previously Published Works  \nTitle  \nMachine learning-guided field site selection for river classification  \nPermalink  \n[https://escholarship.org/uc/item/2024500v](https://escholarship.org/uc/item/2024500v)  \nJournal  \nInternational Journal of Applied Earth Observation and Geoinformation, 142  \nISSN  \n1569-8432  \nAuthors  \nWang, Zhihao Pasternack, Gregory Brian Jin, Yufang  \net al.  \nPublication Date  \n2025-08-01  \nDOI  \n10.1016/j.jag.2025.104742  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nInternational Journal of Applied Earth Observation and Geoinformation 142 (2025) 104742  \nContents lists available at ScienceDirect  \nInternational Journal of Applied Earth Observation and Geoinformation  \njournal [homepage: www.elsevier.com/locate/jag](homepage: www.elsevier.com/locate/jag)  \n| Machine learning-guided field site selection for river classification\u003Cbr>Zhihao Wang a,* , Gregory Brian Pasternacka , Yufang Jin a, Costanza Rampinib, Serena Alexander c , Nikhil Kumar a , Rune Storesund d , K. Martin Peralese ,\u003Cbr>Christopher Limf, Stephanie Moreno g , Igor Lacan h \u003Cbr>a Department of Land, Air and Water Resources, 1 Shields Avenue, University of California, Davis, CA, the United States of America b Department of Environmental Studies, San Jos´e State University, One Washington Square, San Jose, CA 95192, the United States of America c Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, the United States of America\u003Cbr>d SafeR3, 154 Lawson Road, Kensington, CA 94707, the United States of America\u003Cbr>e Napa County Resource Conservation District, 1303 Jefferson Street, Suite 500B, Napa, CA 94559, the United States of America f Contra Costa Resource Conservation District, 2001 Clayton Road, Ste. 200, Concord, CA 94520, the United States of America g North Santa Clara Resource Conservation District, 1560 Berger Drive, Room 211, San Jose, CA 95112, the United States of America\u003Cbr>h University of California Cooperative Extension, San Mateo/San Francisco Counties, 1500 Purissima Creek Road, Half Moon Bay, CA 94019, the United States of America |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>River classification Machine learning Field site selection Prior datasets Uncertainty information |  | Sufficient abundance and variety of field site sampling are crucial for obtaining an accurate reach-scale river classification of a regional stream network in support of scientific research and river management. However, many studies still randomly select field sites or only visit accessible streams. This leads to an inadequate exploration of stream characteristics, resulting in incomplete or inaccurate classification. Machine learning has been recognized for discovering and extracting streams’ geomorphic patterns efficiently and accurately from data, but its application in field site sampling design is still in its infancy. This study developed a general and practical field site selection framework by incorporating machine learning in a human-in-the-loop manner. This framework includes three steps: (1) initial field site selection via machine learning from prior datasets, (2) selected field site accessibility evaluation and observation, and (3) additional field site decision and selection via an iterative learning process. In an example application to the San Francisco Bay Area (California, USA), our framework extracted representative geomorphic characteristics of (i) previous known stream types from prior labeled and geospatial datasets and (ii) previously unrecognized stream types based on uncertainty information obtained by machine learning. Moreover,","cbCaiqJdXXn1DRQI","https://ap.wps.com/l/cbCaiqJdXXn1DRQI","pdf",16460186,1,14,"English","en",105,"# Article I Background\n## Motivation and problem definition\n## Aim and proposed framework\n## Example application and validation","[{\"question\":\"Why is field site sampling crucial for river classification?\",\"answer\":\"Accurate reach-scale classification depends on having sufficient abundance and variety of sampled field sites. Random or access-limited sampling can miss key stream characteristics, leading to incomplete or inaccurate classification.\"},{\"question\":\"What is the proposed machine learning-guided field site selection framework?\",\"answer\":\"The framework uses human-in-the-loop steps: (1) initial site selection via machine learning from prior datasets, (2) accessibility evaluation and observations of selected sites, and (3) additional iterative site decisions using uncertainty information from the model.\"},{\"question\":\"How does the method handle inaccessible field sites?\",\"answer\":\"It proposes methods to replace inaccessible sites while ensuring that sufficient information is retained in the ultimately selected field sites.\"}]","Machine learning-guided field site selection for river classification | PDF",1785684443,35,{"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-guided-field-site-selection-for-river-classification","",{"@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-guided-field-site-selection-for-river-classification/118597/",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","2026-08-02",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 is field site sampling crucial for river classification?","Question",{"text":75,"@type":76},"Accurate reach-scale classification depends on having sufficient abundance and variety of sampled field sites. Random or access-limited sampling can miss key stream characteristics, leading to incomplete or inaccurate classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the proposed machine learning-guided field site selection framework?",{"text":80,"@type":76},"The framework uses human-in-the-loop steps: (1) initial site selection via machine learning from prior datasets, (2) accessibility evaluation and observations of selected sites, and (3) additional iterative site decisions using uncertainty information from the model.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the method handle inaccessible field sites?",{"text":84,"@type":76},"It proposes methods to replace inaccessible sites while ensuring that sufficient information is retained in the ultimately selected field sites.","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"]