[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123260-en":3,"doc-seo-123260-105":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},123260,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Municipal Solid Waste Management Using Machine Learning - A Case Study in Sheger City, Koye Sub-city, Ethiopia","Municipal Solid Waste Management is an increasingly critical challenge in urban areas, intensified by rapid urbanization, population growth, and evolving consumption patterns. This study applies machine learning to predict municipal solid waste generation in Sheger City, Koye Sub-city, Ethiopia, using data from 2009–2023. ARIMA, RF, and LSTM forecast 2024–2028 trends, with LSTM achieving the best performance (MSE 1.62×10^8, MAE 9,500, R² 0.93). Results indicate an 8.5% rise overall and stronger growth in food and plastic waste, supporting improved recycling and policy interventions.","Municipal Solid Waste Management Using Machine Learning: A Case Study in Sheger City, Koye Sub-city, Ethiopia  \nTsegaye Hordofa Gudeta 􀀍   \nCollege of Environmental Science and Engineering,  \nTongji University, Shanghai 200092, China  \nGudeta Tesema Mamo   \nCollege of Environmental Science and Engineering,  \nTongji University, Shanghai 200092, China  \nYezeshawal Mengistu Neguse   \nCollege of Environmental Science and Engineering,  \nTongji University, Shanghai 200092, China  \nArticle History:  \nReceived: 08.03.2025 Revised: 28.03.2025 Accepted: 01.04.2025 Published: 11.04.2025  \nAbstract  \nMunicipal Solid Waste Management is an increasingly critical challenge in urban areas, intensified by rapid urbanization, population growth, and evolving consumption patterns. This study investigates the application of machine learning techniques to predict municipal solid waste generation in Sheger City, Koye Sub-city, Ethiopia, using data from 2009 to 2023. Three machine learning models, ARIMA, RF, and LSTM, were employed to forecast waste generation trends for the period 2024–2028, considering various socio-economic and demographic factors. Among the models, LSTM demonstrated the highest accuracy, with MSE of 1.62 × 10⁸ tonnes, MAE of 9,500 tonnes, and R² of 0.93. These results outperformed ARIMA (MSE = 3.84 × 10⁸ tonnes², MAE = 15,200 tonnes, R² = 0. 85) and RF (MSE = 2.91 × 10⁸ tonnes², MAE = 12,800 tonnes, R² = 0. 89) . The forecasts predict an 8. 5% increase in total waste generation, from 3,852,150 tonnes in 2023 to 4,177,500 tonnes by 2028. Notable growth is expected in high-volume waste streams, including food waste (13.5% increase) and plastic waste (8.9% increase). These findings highlight the urgent need for enhanced waste management strategies, including expanded recycling programs and policy interventions. This study provides a robust framework for leveraging machine learning models to guide waste management decisions, contributing to more sustainable urban waste management practices in rapidly growing cities.  \nKeywords: Solid Waste Management, Machine Learning, Random Forest, Waste Generation, Waste Prediction. Suggested citation: Gudeta, T.H., Mamo, G.T., & Neguse, Y.M. (2025). Municipal Solid Waste  \nManagement Using Machine Learning: A Case Study in Sheger City, Koye Sub-city, Ethiopia. European Journal of Theoretical and Applied Sciences, 3(2), 511-525. [https://doi.org/10.59324/ejtas.2025.3](https://doi.org/10.59324/ejtas.2025.3) (2).42  \nIntroduction  \nMunicipal Solid Waste Management (MSWM) is a critical concern in urban areas, particularly as cities worldwide experience rapid population  \ngrowth and urbanization. The World Bank estimates that by 2025, urban areas will generate approximately 2.2 billion tons of waste annually, with developing countries facing the most significant challenges in managing this waste  \neffectively (Alshaikh & Abdelfatah, 2024). Inefficient waste management systems can lead to adverse environmental and public health outcomes, making adopting innovative strategies for sustainable waste management essential.  \nPredictive analytics leverages historical data and statistical algorithms to forecast future trends. In waste management, predictive analytics is a powerful tool for optimizing waste collection, improving resource allocation, and enhancing the overall efficiency of waste management systems. Studies have demonstrated that predictive analytics can lead to significant cost savings and improved service delivery. The benefits of predictive analytics include enhanced decision-making, as it provides actionable insights that enable waste management authorities to make informed decisions regarding collection schedules, resource allocation, and policy formulation (Aphale et al., 2015) . Additionally, authorities can optimize collection routes and schedules by forecasting waste generation, reducing operational costs, and minimizing environmental impacts (Alsabtet al., 2024). Furthermore, predictiv","cbCaihe0fNehSTdh","https://ap.wps.com/l/cbCaihe0fNehSTdh","pdf",489528,1,15,"English","en",105,"# Abstract\n# Introduction\n## Urbanization and waste generation drivers\n## Predictive analytics in waste management\n## Demographic and socio-economic factors\n# Research focus and case study context","[{\"question\":\"Which machine learning models are used to forecast municipal solid waste generation?\",\"answer\":\"The study employs three models: ARIMA, Random Forest (RF), and LSTM to forecast waste generation trends for 2024–2028.\"},{\"question\":\"How does LSTM perform compared with ARIMA and RF?\",\"answer\":\"LSTM delivers the highest accuracy, with MSE 1.62×10^8, MAE 9,500 tonnes, and R² of 0.93, outperforming ARIMA and RF based on the reported metrics.\"},{\"question\":\"What waste generation changes are predicted for Sheger City by 2028?\",\"answer\":\"The forecasts predict an 8.5% increase in total waste generation from 3,852,150 tonnes in 2023 to about 4,177,500 tonnes by 2028, with notable growth expected in food and plastic waste streams.\"}]","Municipal Solid Waste Management Using Machine Learning - A Case Study in Sheger City, Koye Sub-city, Ethiopia | PDF",1785815527,38,{"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},"municipal-solid-waste-management-using-machine-learning-a-case-study-in-sheger-city-koye-sub-city-ethiopia","",{"@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/municipal-solid-waste-management-using-machine-learning-a-case-study-in-sheger-city-koye-sub-city-ethiopia/123260/",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},"Which machine learning models are used to forecast municipal solid waste generation?","Question",{"text":75,"@type":76},"The study employs three models: ARIMA, Random Forest (RF), and LSTM to forecast waste generation trends for 2024–2028.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does LSTM perform compared with ARIMA and RF?",{"text":80,"@type":76},"LSTM delivers the highest accuracy, with MSE 1.62×10^8, MAE 9,500 tonnes, and R² of 0.93, outperforming ARIMA and RF based on the reported metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"What waste generation changes are predicted for Sheger City by 2028?",{"text":84,"@type":76},"The forecasts predict an 8.5% increase in total waste generation from 3,852,150 tonnes in 2023 to about 4,177,500 tonnes by 2028, with notable growth expected in food and plastic waste streams.","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"]