[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123513-en":3,"doc-seo-123513-105":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},123513,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Application of Machine Learning for Prediction of Solid Waste Generation","Effective solid waste management depends on accurate forecasting of waste generation and composition to support adaptive planning and timely resource allocation. Reliable predictions require understanding the impacts of population dynamics, urbanization, income levels, and temporal factors that shape waste trends. Traditional approaches such as regression and fixed-effects analysis often struggle with complex non-linear relationships. Machine learning techniques leverage large datasets and advanced computation to improve predictive performance, while ongoing constraints such as data scarcity, regional variability, and scalability limit real-world adoption. This review summarizes key influencing factors and highlights opportunities for real-time waste monitoring by combining ML with emerging technologies such as IoT.","Natural Built Social Environment Health  \nISSN 3085-461X  \n[www.nbseh.org](www.nbseh.org)  \nApplication of Machine Learning for Prediction of Solid Waste Generation  \nAjaya Subedia, Sahil Shresthab, and Shukra Raj Paudelc*  \nEnvironmental Engineering Programme, Department of Civil Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur 44700, Nepal  \na ORCID: 0009-0008-3691-250X; Email: [079msene003.ajaya@pcampus.edu.np](079msene003.ajaya@pcampus.edu.np)  \nbORCID: 0009-0001-3624-3130; Email: [079msene019.sahil@pcampus.edu.np](079msene019.sahil@pcampus.edu.np)  \ncORCID: 0000-0002-5764-2990; Email: [srpaudel@ioe.edu.np](srpaudel@ioe.edu.np)  \n*Corresponding author: Shukra Raj Paudel, Email address: [srpaudel@ioe.edu.np](srpaudel@ioe.edu.np)  \nGraphical Abstract  \nThis article provides a comprehensive review of both standalone and hybrid machine learning (ML) methodologies, emphasizing their applications in waste generation forecasting. It further examines the critical factors influencing waste generation, including demographic, regulatory, socio-economic and temporal characteristics. The use of ML in solid waste prediction modelling results from the growing complexity of influencing parameters and the expanding availability of relevant datasets.  \nDOI: 10.63095/NBSEH.25.831599  \nAvailable Online 12 April 2025  \nCitation: Subedi, A., Shrestha, S., & Paudel, S. R., 2025. Application of Machine Learning for Prediction of Solid Waste Generation. Natural Built Social Environment Health 1(2), 14-33.  \nDOI: 10.63095/NBSEH.25.831599  \nAbstract: Effective solid waste management requires adaptive planning and accurate forecasting of waste generation and composition. Accurate predictions require a deep understanding of key influencing factors, including population, urbanization, income, and temporal effects, which impact waste trends. Traditional models, including regression and fixed-effects analysis, struggle to capture these complex, non-linear relationships. Recently, machine learning (ML) techniques have emerged as powerful tools, leveraging large datasets and advanced computational methods to improve predictive accuracy. Nonetheless, challenges like data limitations, regional variability, and scalability hinder their practical implementation. This review explores and summarizes the significance of the essential factors for predicting solid waste generation and emphasizes new opportunities for real-time waste monitoring by integrating ML with emerging technologies like the Internet of Things (IoT) . Enhanced sustainable waste management needs advances in model generalisability, data quality, and policy integration.  \nKeywords: Solid Waste, Machine Learning, Prediction Model  \nIntroduction  \nSolid waste generation is a pressing global challenge, with profound environmental, economic, and public health implications. Currently, about 2 billion tonnes of municipal solid wastes (MSW) are produced globally per year, and it is projected to rise to 3.4 billion tonnes by 2050—driven by rapid urbanization, population growth, and industrialization [1] . This unregulated and poorly managed waste disposal greatly worsens pollution, impacting air, soil, and water quality, leading to severe health issues, environmental degradation, and contributing to greenhouse gas emissions, consequently amplifying climate change effects [2, 3] . For instance, untreated waste near freshwater sources introduces harmful contaminants, causing widespread pollution and threatening aquatic ecosystems [4, 5] . Similarly, the growing complexity of waste composition makes treatment more difficult [6, 7] . Allocating adequate resources for waste collection, and determining waste collection routes necessitate adequate spatial, population, and waste density data. Similarly, the selection and management of landfill sites depend on the availability of data on solid waste generation and composition. The economic viability of material recovery facil","cbCaifLzF0mdAAA2","https://ap.wps.com/l/cbCaifLzF0mdAAA2","pdf",1002565,1,20,"English","en",105,"# Introduction\n## Importance of solid waste forecasting\n## Limitations of traditional models\n## Need for multidimensional predictors\n## Complexity of industrial waste prediction\n# Graphical Abstract and Review Scope\n## Standalone and hybrid ML methods\n## Influencing factors and data drivers\n## Challenges and future opportunities","[{\"question\":\"Why is accurate forecasting of solid waste generation critical for waste management?\",\"answer\":\"Accurate forecasting enables waste management authorities to plan collection and treatment resources efficiently, design effective policy interventions, and reduce risks of underestimating waste volumes that can cause pollution and infrastructure inefficiencies.\"},{\"question\":\"What challenges do traditional predictive models face in solid waste forecasting?\",\"answer\":\"Traditional methods such as correlation analysis and regression struggle to capture complex, non-linear relationships between waste production and socio-economic or demographic variables, and they often rely on limited input predictors.\"},{\"question\":\"How does machine learning improve prediction of solid waste generation?\",\"answer\":\"Machine learning uses large datasets and advanced computational techniques to model complex patterns more effectively than traditional statistical models. It can support improved predictive accuracy, although issues like data limitations, regional variability, and scalability still hinder practical implementation.\"}]","Application of Machine Learning for Prediction of Solid Waste Generation | PDF",1785816992,50,{"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},"application-of-machine-learning-for-prediction-of-solid-waste-generation","",{"@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/application-of-machine-learning-for-prediction-of-solid-waste-generation/123513/",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","2026-08-04",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 accurate forecasting of solid waste generation critical for waste management?","Question",{"text":75,"@type":76},"Accurate forecasting enables waste management authorities to plan collection and treatment resources efficiently, design effective policy interventions, and reduce risks of underestimating waste volumes that can cause pollution and infrastructure inefficiencies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenges do traditional predictive models face in solid waste forecasting?",{"text":80,"@type":76},"Traditional methods such as correlation analysis and regression struggle to capture complex, non-linear relationships between waste production and socio-economic or demographic variables, and they often rely on limited input predictors.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning improve prediction of solid waste generation?",{"text":84,"@type":76},"Machine learning uses large datasets and advanced computational techniques to model complex patterns more effectively than traditional statistical models. 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