[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118756-en":3,"doc-seo-118756-105":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},118756,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Short-term water demand forecasting using data-centric machine learning approaches","Accurate short-term water demand forecasting supports urban water management by reducing system stress from urbanisation, water scarcity, and climate change. The study evaluates a data-centric machine learning perspective that examines how training data length, temporal resolution, and data uncertainty influence forecasting accuracy. Forecasting performance is tested across ARIMA, neural network, random forest, and Prophet models. Case studies indicate strong results even with short training data and that data quality improvements can yield accuracy gains comparable to model-centric tuning.","corrected Proof  \n© 2023 The Authors Journal of Hydroinformatics Vol 00 No 0, 1 doi: 10 .2166/hydro.2023.163  \nShort-term water demand forecasting using data-centric machine learning approaches  \nGuoxuan Liu a, Dragan Savic a, b, c and Guangtao Fu a, *  \na Centre for Water Systems, University of Exeter, Exeter, EX4 4QF, United Kingdom b KWR Water Research Institute, Nieuwegein, 3430 BB, The Netherlands  \nc Faculty of Civil Engineering, University of Belgrade, Bul. Kralja Aleksandra 73, 11120 Belgrade, Serbia  \n*Corresponding author. E-mail: [g.fu@exeter.ac.uk](g.fu@exeter.ac.uk)  \n GL, 0000-0001-9837-8252; DS, 0000-0001-9567-9041  \nABSTRACT  \nAccurate water demand forecasting is the key to urban water management and can alleviate system pressure brought by urbanisation, water scarcity and climate change. However, existing research on water demand forecasting using machine learning is focused on model-centric approaches, where various forecasting models are tested to improve accuracy. The study undertakes a data-centric machine learning approach by analysing the impact of training data length, temporal resolution and data uncertainty on forecasting model results. The models evaluated are Autoregressive (AR) Integrated Moving Average (ARIMA), Neural Network (NN), Random Forest (RF) and Prophet. The first two are commonly used forecasting models. RF has shown similar forecast accuracy to NN but has received less attention. Prophet is a new model that has not been applied to short-term water demand forecasting, though it has had successful applications in various fields. The results obtained from four case studies show that (1) data-centric machine learning approaches offer promise for improving forecast accuracy of short-term water demands; (2) accurate forecasts are possible with short training data; (3) RF and NN models are superior at forecasting high-temporal resolution data; and (4) data quality improvement can achieve a level of accuracy increase comparable to model-centric machine learning approaches.  \nKey words: autoregressive integrated moving average, data-centric machine learning, neural network, prophet, random forecast, short-term water demand forecasting  \nHIGHLIGHTS  \n• Data-centric machine learning approaches offer promise for improving the forecast accuracy of short-term water demands.  \n• Accurate forecasts are possible with short training data.  \n• Random forest and neural network models are superior at forecasting high-temporal resolution data.  \n• Data quality improvements can achieve a level of accuracy increase comparable to model-centric machine learning approaches.  \nINTRODUCTION  \nWater demand management is essential for ensuring water security in urban centres, which are increasingly coming under threat due to urbanisation, water scarcity and climate change. An effective way to mitigate the increasing threat is to make accurate demand and consumption forecasts, for short, medium and long forecasting horizons; these different horizonsaid utilities with operation, financing and planning-related issues (Donkor et al. 2014) . For operational management, shortterm water demand forecasting is vital for pump operation and early leakage detection, which are key to improving service quality and minimising water loss. For demand forecasting to be successfully used for leakage detection, a high level of accuracy is necessary (Wu & Liu 2017) .  \nWith an aim to improve the accuracy of short-term water demand forecasting, much of the work has been using model-centric approaches (Lertpalangsunti et al. 1998 ; Herrera et al. 2010 ; Adamowski et al. 2012 ; Chen et al. 2017 ; Gagliardi et al. 2017 ; Chen & Boccelli 2018 ; Sardinha-Lourenço et al. 2018 ; Liu et al. 2022) . These approaches focus on developing and adapting models to data, through various approaches including parameter optimisation, alterations to model structure and ensemble models. For machine learning models with well-defined structures, such as the Prophet","cbCaib2iGetVyBhC","https://ap.wps.com/l/cbCaib2iGetVyBhC","pdf",1264292,1,17,"English","en",105,"# Introduction\n## Water demand management and forecasting horizons\n## Importance of short-term forecasts for operations and leakage detection\n## Model-centric vs data-centric machine learning approaches","[{\"question\":\"Why is short-term water demand forecasting important for urban utilities?\",\"answer\":\"It enables effective pump operation and supports early leakage detection, improving service quality and reducing water loss. High accuracy is essential for leakage detection use cases.\"},{\"question\":\"What factors does the study focus on in the data-centric machine learning approach?\",\"answer\":\"Training data length, temporal resolution, and data uncertainty are analyzed to understand their impact on forecasting model results.\"},{\"question\":\"Which forecasting models are evaluated and what key findings are reported?\",\"answer\":\"ARIMA, neural networks, random forest, and Prophet are compared. Results show promising accuracy improvements with data-centric methods, accurate forecasts with short training data, and stronger performance of RF and NN at high temporal resolution data.\"}]","Short-term water demand forecasting using data-centric machine learning approaches | PDF",1785720077,43,{"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},"short-term-water-demand-forecasting-using-data-centric-machine-learning-approaches","",{"@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/short-term-water-demand-forecasting-using-data-centric-machine-learning-approaches/118756/",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","2026-08-03",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 short-term water demand forecasting important for urban utilities?","Question",{"text":75,"@type":76},"It enables effective pump operation and supports early leakage detection, improving service quality and reducing water loss. High accuracy is essential for leakage detection use cases.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What factors does the study focus on in the data-centric machine learning approach?",{"text":80,"@type":76},"Training data length, temporal resolution, and data uncertainty are analyzed to understand their impact on forecasting model results.",{"name":82,"@type":73,"acceptedAnswer":83},"Which forecasting models are evaluated and what key findings are reported?",{"text":84,"@type":76},"ARIMA, neural networks, random forest, and Prophet are compared. 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