[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124920-en":3,"doc-seo-124920-105":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},124920,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Demystifying Machine Learning: Applications in African Environmental Science and Engineering","The article examines the transformative role of Machine Learning (ML) in Environmental Science and Engineering (ESE), detailing how ML supports improved decision-making and operational efficiency across climate change, energy management, and water resource management. It highlights ML methods including regression analysis, anomaly detection, and deep learning, and explains their relevance to complex environmental problems. Special attention is given to African contexts, where infrastructure constraints and data scarcity limit adoption. Proposed paths include cloud computing and lightweight models, while real applications in meteorology, energy optimization, and water management demonstrate measurable gains. The work also covers methodological needs such as model selection and rigorous benchmarking, outlining capabilities, barriers, and future directions.","Demystifying Machine Learning: Applications in African Environmental Science and Engineering  \nTadiwa Walter Muparutsa 􀀍  \nCollege of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China UNEP-Tongji Institute of Environmentfor Sustainable Development, 1239 Siping Road, Shanghai 200092, China  \n\n| Suggested Citation |\n| --- |\n| Muparutsa, T.W. (2024) .\u003Cbr>Demystifying Machine Learning: Applications in African Environmental Science and Engineering. European Journal of Theoretical and Applied Sciences, 2(3), 688-705.\u003Cbr>DOI: 10.59324/ejtas.2024.2(3).53 |\n\nAbstract:  \nThis article delves into the transformative role of Machine Learning (ML) in Environmental Science and Engineering (ESE), illustrating its broad applications across diverse environmental issues and its potential to enhance decision-making and operational efficiency. It emphasizes the integration of ML techniques such as regression analysis, anomaly detection, and deep learning to address complex challenges in climate change, energy management, water resource management, and more. The document particularly focuses on the adaptation and challenges of ML in the African context, highlighting barriers such as infrastructure limitations and data scarcity, while  \nproposing innovative solutions like cloud computing and lightweight models. Practical use cases in meteorology, energy optimization, and water management underscore the practical impacts of ML, showing significant advancements in forecasting, resource management, and system optimization. The article also discusses methodological considerations necessary for effective ML application in ESE, including model selection and rigorous benchmarking. Ultimately, it provides a comprehensive overview of current capabilities, challenges, and future directions for ML in environmental science, advocating for continued innovation and tailored solutions to meet the unique needs of different regions, particularly Africa.  \nKeywords: Machine Learning, Environmental Science and Engineering, Climate Change, Data Analysis, Sustainable Solutions.  \nIntroduction  \nData science is experiencing a global paradigm shift, with Artificial Intelligence (AI) and Machine Learning (ML) redefining our interactions with and understanding of data (Borenstein & Howard, 2021; Carlos J. Costa & Manuela Aparicio, 2023). Covering a wide range of methodologies from basic database management to advanced analytics, data science integrates techniques from statistics, computer science, and information theory to extract  \ninsights from both structured and unstructured data (Sarker, 2021) .  \nAI, aiming to replicate human cognitive functions through machines, involves sophisticated tasks such as visual perception, speech recognition, decision-making, and language translation (Carlos J Costa & Manuela Aparicio, 2023). Machine Learning, as an integral part of AI, concentrates on crafting algorithms that allow models to autonomously learn and make inferences from data. Machine Learning, a crucial subset of AI, focuses on developing  \nalgorithms that enable models to learn from data independently. This capability is pivotal across various sectors including Environmental Science and Engineering (ESE), healthcare, and finance, driving significant advancements and  \ninnovations. (V. De Faria, A. De Queiroz, L. Lima, J. Lima, & B. Da Silva, 2022; [V. A. D. de](V. A. D. de)[ ](V. A. D. de)[Faria](Faria), A. R. de Queiroz, L. M. Lima, J. W. M. Lima, & B. C. da Silva, 2022; Zhong et al., 2021) .  \nFigure 1. Overlaps and Distinctions in the Relationships Between AI, Machine Learning, and Statistics  \nSource: Hsieh, 2022  \nThe use of Machine Learning (ML) in Environmental Science and Engineering (ESE) is driven by human-induced disturbances and climate stressors, which are harming the planet's health. These factors have changed our environments and ecosystems, leading to environmental degradation issues such as biodiversity loss","cbCaitJwhhNHhvaU","https://ap.wps.com/l/cbCaitJwhhNHhvaU","pdf",521235,1,18,"English","en",105,"# Introduction\n## Machine Learning in Environmental Science and Engineering\n# Global Trends and Practices","[{\"question\":\"How does machine learning contribute to environmental science and engineering decision-making?\",\"answer\":\"Machine learning improves decision-making and operational efficiency by extracting patterns from complex datasets. The document links ML use to better forecasting and resource management in environmental systems.\"},{\"question\":\"Which machine learning techniques are emphasized for ESE problems?\",\"answer\":\"The document emphasizes regression analysis, anomaly detection, and deep learning. These techniques are described as suitable for complex challenges in climate change, energy management, and water resources.\"},{\"question\":\"What challenges limit ML adoption in Africa, and what solutions are proposed?\",\"answer\":\"The document highlights barriers such as limited infrastructure and data scarcity. It proposes solutions including cloud computing and lightweight models to make ML more feasible in the African context.\"}]","Demystifying Machine Learning: Applications in African Environmental Science and Engineering | PDF",1785895389,45,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"demystifying-machine-learning-applications-in-african-environmental-science-and-engineering","",{"@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/demystifying-machine-learning-applications-in-african-environmental-science-and-engineering/124920/",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},"How does machine learning contribute to environmental science and engineering decision-making?","Question",{"text":75,"@type":76},"Machine learning improves decision-making and operational efficiency by extracting patterns from complex datasets. 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