[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119514-en":3,"doc-seo-119514-105":30,"detail-sidebar-cat-0-en-105":92},{"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},119514,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Assessing Surface Water Pollution in Hanoi, Vietnam Using Remote Sensing and Machine Learning Algorithms","Rapid urbanization drives major land-use changes and intensifies surface water pollution worldwide, with Hanoi in Vietnam experiencing chronic degradation for more than a decade. This study develops a tracking approach using available technologies to monitor key pollutants through earth observation and machine learning. A cubist-based model (ML-CB) integrates optical and RADAR satellite data to estimate TSS, COD, and BOD, trained on Sentinel imagery and validated against field surveys. Results show significant predictive performance, supporting practical water-quality monitoring for cities in the Global South.","1 2  \n3 4  \n5  \n6 7  \n8 9  \n10  \n11  \n12  \n13  \n14  \n15  \n16  \n17  \n18  \n19  \n20  \n21  \n22  \n23  \n24  \n25  \n26  \n27  \n28  \n29  \n30  \n31  \n32  \n33  \n34  \n35  \n36  \n37  \n38  \n39  \nAssessing surface water pollution in Hanoi, Vietnam using remote sensing and machine learning algorithms  \nThi-Nhung Do1, Diem-My Thi Nguyen1, Jiwnath Ghimire3, Kim-Chi Vu2, Lam-Phuong Do Dang1, Sy-Liem Pham1, Van-Manh Pham1*  \n(1) Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam  \n(2) VNU Institute of Vietnamese Studies and Development Science, Vietnam National University, Hanoi, 336 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam  \n(3) Department of Community and Regional Planning, Iowa State University, 715 Bissell Road, Ames, Iowa, USA  \n* Corresponding Author: Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Thanh  \nXuan, Hanoi, Vietnam; [e-mail: manh10101984@gmail.com](e-mail: manh10101984@gmail.com)  \nAbstract  \nRapid urbanization leads to significant land-use changes and poses threats to surface water bodies worldwide, especially in the Global South. Hanoi, the capital city of Vietnam, has been facing chronic surface water pollution for more than a decade. Developing a methodology to better track and analyze pollutants using available technologies to manage the problem has been imperative. Advancement of machine learning and earth observation systems offers opportunities for tracking water quality indicators, especially the increasing pollutants in the surface water bodies. This study introduces machine learning with the cubist model (ML-CB), which combines optical and RADAR data and a machine-learning algorithm to estimate surface water pollutants, including total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD). The model was trained using optical (Sentinel-2A and Sentinel-1A) and RADAR satellite images. Results were compared with field survey data using regression models. Results show that the predictive estimates of pollutants based on ML-CB provide significant results. The study offers an alternative water quality monitoring method for managers and urban planners, which could be instrumental in protecting and sustaining the use of surface water resources in Hanoi and other cities of the Global South.  \nKEYWORDS: Remote sensing; Machine learning; Surface water pollution; Water quality parameters; Hanoi City  \n1. Introduction  \nSurface water bodies are crucial for terrestrial ecosystems and human activities (Ahamad et al. 2019; Falkenmark 2020; Sánchez-Zarco et al. 2020; Cheng 2022). They offer both aesthetic and environmental values for urban areas. However, surface water is contaminated in many regions of the world, particularly in the metropolitan regions of developing countries (Loucks and van Beek 2017) , due to unplanned urbanization (Chen et al., 2022) . Although rivers are facilitating inclusive and environmentally sustainable development in cities (Tickner et al. 2017; Anderson et al. 2019; Fang and Jawitz 2019), the accelerating population density along the river corridors has led to a series of environmental and social problems (McDonald et al. 2014; Alamri et al. 2021; Ma et al. 2022) . The riverfront development projects that include public parks, walkways, and cycle paths can encourage physical activity and social interaction . Urban water bodies can help reduce the urban heat island effects by providing natural cooling and ventilation. In contrast, these water bodies are being taken over by additional developments, wastewater, and toxic pollution, especially in cities of the Global South (citation) .  \n40 Uncontrolled urban expansions and the release of untreated wastewater from industrial and domestic uses to rivers and 41 ponds are the major water pollution sources in developing countries (McGrane 2016; Liyanage and Yamada 2017; 42 Bashir et al. 2020) . Illegal discharges from treatment plants, ","cbCailO7nH15s1hx","https://ap.wps.com/l/cbCailO7nH15s1hx","pdf",1261469,1,18,"English","en",105,"# Introduction\n## Surface water importance and contamination drivers\n## Limitations of traditional monitoring methods\n## Remote sensing for water quality estimation","[{\"question\":\"What pollutants does the ML-CB model estimate in Hanoi’s surface waters?\",\"answer\":\"The model estimates total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD).\"},{\"question\":\"Which satellite data sources are used to train the proposed machine learning approach?\",\"answer\":\"Training uses optical imagery from Sentinel-2A and RADAR imagery from Sentinel-1A.\"},{\"question\":\"How are the model results evaluated in the study?\",\"answer\":\"Predicted pollutant estimates are compared with field survey data using regression models to assess performance.\"}]","Assessing Surface Water Pollution in Hanoi, Vietnam Using Remote Sensing and Machine Learning Algorithms | PDF",1785724725,45,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"assessing-surface-water-pollution-in-hanoi-vietnam-using-remote-sensing-and-machine-learning-algorithms","",{"@graph":36,"@context":86},[37,54,69],{"@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/assessing-surface-water-pollution-in-hanoi-vietnam-using-remote-sensing-and-machine-learning-algorithms/119514/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What pollutants does the ML-CB model estimate in Hanoi’s surface waters?","Question",{"text":76,"@type":77},"The model estimates total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which satellite data sources are used to train the proposed machine learning approach?",{"text":81,"@type":77},"Training uses optical imagery from Sentinel-2A and RADAR imagery from Sentinel-1A.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the model results evaluated in the study?",{"text":85,"@type":77},"Predicted pollutant estimates are compared with field survey data using regression models to assess performance.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]