[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125199-en":3,"doc-seo-125199-105":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},125199,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Estimating household water storage from images - A machine learning approach","Research on achieving SDG 6.1 has focused more on safe access to drinking water than on risks in safe transport, storage, and use of collected water. Few high-quality datasets quantify how many household containers are used and the volumes they hold. This paper applies machine learning to 2022 domestic water-storage images from southern Bangladesh, identifying container types and estimating storage with over 90% accuracy. Results enable rapid creation of a quantitative, high-resolution dataset with counts and individual and aggregated volumes, informing study-location policy and future household water insecurity work.","corrected Proof  \n© 2025 The Authors Journal of Water, Sanitation and Hygiene for Development Vol 00 No 0, 1 doi: 10 .2166/washdev.2025.260 Short Communication  \nEstimating household water storage from images: A machine learning approach  \nChad Staddon a, *, Ajmal Shahbazb, Syed U. Yunasb, Lyndon Smithb, Geraint Burrowsc,  \nSayed Mohammed Nazim Uddind, e and Lucy Whitleyc  \na Centre for Water, Communities & Resilience, University of the West of England, Bristol BS16 1QY, UK  \nb Centre for Machine Vision, Bristol Robotics Lab, University of the West of England, Coldharbour Lane, Bristol, BS16 1QY, UK c Groundwater Relief, Dartington Hall, Totnes, Devon TQ9 6EQ, UK  \nd IMDEA Water Institute, Avda. Punto Com 2, 28805 Alcalá de Henares, Madrid, Spain  \ne Center for Climate Change and Environmental Health (3CEH), Asian University for Women, 4000, Chattogram, Bangladesh  \n*Corresponding author. E-mail: [chad.staddon@uwe.ac.uk](chad.staddon@uwe.ac.uk)  \n CS, 0000-0002-2063-8525  \nABSTRACT  \nWhilst much research focusses on challenges related to achieving SDG 6.1 (universal and equitable access to safe and affordable drinking water), there has been less attention to challenges of safe transport, storage and use of collected water. In particular, there are relatively few high-quality datasets quantifying the number and volume of water containers used by households for such purposes. This paper reports results from the application of machine learning (ML) techniques to a database of images of domestic water storage collected during 2022 as part of an initiative to improve water supply in southern Bangladesh. Because the number of different water container types was relatively small, it was possible to train an ML algorithm to identify water containers and estimate water storage with greater than 90% accuracy. These results have allowed the rapid creation of a unique high-quality, high-resolution dataset describing water storage quantitatively in a study community. This dataset includes data quantifying the number of vessels as well as their individual and aggregated water storage volumes. The paper discusses policy implications for the study location specifically before concluding with suggestions for the inclusion of this sort of analysis in ongoing studies of household and community scale water insecurity.  \nKey words: Bangladesh, household water storage, machine learning, SDG 61  \nHIGHLIGHTS  \n• Machine learning can speed up the processing of images of water storage taken during conventional household water access questionnaires.  \n• Accurate quantification of available water storage can assist in water services programme planning.  \n• Machine learning-assisted analysis of over 800 images collected during household research in Bangladesh in 2022 showed that the average storage per person is only 2 . 5 L.  \nINTRODUCTION  \nIn water-insecure communities, the humble (often yellow) ‘jerry can’ is a familiar sight, often alongside a jumble of other (usually plastic) water storage containers (Figure 1) . In 2022, for example, there were over 1.5 billion people worldwide who were dependent on water provisioning arrangements that required collection and transport from remote locationsand storage in the home prior to use (GBD 2016 Diarrhoeal Disease Collaborators 2018; Prüss-Ustün et al. 2019 ; Staddon & Brewis 2024) . Fetching water from the source to home with these commonly used containers generates immediate risks from the journey itself as well as risks associated with subsequent management of transported and stored water. In addition to chronic musculoskeletal problems caused by repeatedly carrying water even short distances, transporting water manually also carries with it a higher likelihood of acute injury, particularly bone fractures and muscle and ligament damage (Geereet al. 2010) . Research has also found that fetched and then stored water is very often contaminated with microbiological and other contaminants within 12 h of collecti","cbCaia1VWzvpTuhN","https://ap.wps.com/l/cbCaia1VWzvpTuhN","pdf",477036,1,9,"English","en",105,"# Abstract\n# Highlights\n# Introduction\n## Water storage containers and water insecurity\n## Limits of self-reported questionnaires\n## Value of machine-learning image analysis","[{\"question\":\"Why is household water storage underrepresented in water research and policy?\",\"answer\":\"Water and sanitation studies often emphasize access to safe drinking water, while storage container-related evidence is limited in both research and policy literature. The paper notes that very few high-quality datasets quantify household container counts and volumes.\"},{\"question\":\"What does the machine learning approach achieve in this study?\",\"answer\":\"The approach trains a model to identify domestic water container types and estimate household water storage from images. Reported performance exceeds 90% accuracy for identifying containers and estimating storage.\"},{\"question\":\"What dataset is produced and how can it be used?\",\"answer\":\"The study enables rapid creation of a unique, high-quality, high-resolution dataset capturing the number of vessels and both individual and aggregated water storage volumes. It supports planning and can be incorporated into ongoing household and community water insecurity studies.\"}]","Estimating household water storage from images - A machine learning approach | PDF",1785897338,23,{"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},"estimating-household-water-storage-from-images-a-machine-learning-approach","",{"@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/estimating-household-water-storage-from-images-a-machine-learning-approach/125199/",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},"Why is household water storage underrepresented in water research and policy?","Question",{"text":75,"@type":76},"Water and sanitation studies often emphasize access to safe drinking water, while storage container-related evidence is limited in both research and policy literature. The paper notes that very few high-quality datasets quantify household container counts and volumes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the machine learning approach achieve in this study?",{"text":80,"@type":76},"The approach trains a model to identify domestic water container types and estimate household water storage from images. Reported performance exceeds 90% accuracy for identifying containers and estimating storage.",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset is produced and how can it be used?",{"text":84,"@type":76},"The study enables rapid creation of a unique, high-quality, high-resolution dataset capturing the number of vessels and both individual and aggregated water storage volumes. 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