[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117473-en":3,"doc-seo-117473-105":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},117473,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning for Environmental Sustainability","Research proposes a comprehensive machine-learning framework to tackle urgent environmental sustainability challenges across agriculture and forest ecosystems, with focus on agricultural residue management. Machine learning models support analysis, prediction of ecological phenomena, and optimization of resource-management decisions. The study investigates crop residue management, soil CO2 flux prediction, and forest carbon system prediction. It evaluates multiple ML approaches, including random forests, support vector machines, and ensemble learning, emphasizing strengths and limitations, and outlines emerging trends and future research directions.","Graduate Theses, Dissertations, and Problem Reports  \n2024  \nMachine Learning for Environmental Sustainability Syeda Nyma Ferdous  \nWest Virginia University  \nFollow this and additional works at: [https://researchrepository.wvu.edu/etd](https://researchrepository.wvu.edu/etd)  \n Part of the Other Computer Engineering Commons  \nRecommended Citation  \nFerdous, Syeda Nyma, \"Machine Learning for Environmental Sustainability \" (2024) . Graduate Theses, Dissertations, and Problem Reports. 12374.  \n[https://researchrepository.wvu.edu/etd/12374](https://researchrepository.wvu.edu/etd/12374)  \nThis Dissertation is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s) . You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Dissertation has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [researchrepository@mail.wvu.edu](researchrepository@mail.wvu.edu).  \nMachine Learning for Environmental  \nSustainability  \nSyeda Nyma Ferdous  \nDissertation submitted to the  \nStatler College of Engineering and Mineral Resources at West Virginia University  \nin partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy in  \nComputer Engineering  \nXin Li, Ph.D., Chair  \nNatalia Schmid, Ph.D.  \nMatthew Valenti, Ph.D.  \nBrian Powell, Ph.D.  \nShuo Wang, Ph.D.  \nLane Department of Computer Science and Electrical Engineering  \nMorgantown, West Virginia  \n2024  \nKeywords: Deep Ensemble Model, Soil Conditioning Index, Organic Matter Factor, Soil Erosion Factor, Orthogonal Regularization, soil carbon, biogeochemical model, carbon dioxide, forest ecosystem, ensemble learning,  \nsoil CO2 flux, Meta-heuristic algorithm  \nCopyright © 2024 Syeda Nyma Ferdous  \nAbstract  \nMachine Learning for Environmental Sustainability Syeda Nyma Ferdous  \nThis research proposes a comprehensive approach to address pressing challenges in environmental sustainability, agricultural residue management, using machine learning based approaches. Machine learning (ML) techniques have emerged as powerful tools for addressing environmental sustainability challenges by facilitating the analysis and prediction of ecological phenomena, and optimization of resource management strategies. The study explores the synergies between environmental sustainability and machine learning to develop a framework that leverages artificial intelligence techniques covering a wide range of tasks including crop residue management, soil CO2 flux prediction, and forest carbon system prediction for sustainable development. The study analyze various ML models, such as, random forests, support vector machines, and ensemble learning techniques, highlighting their strengths and limitations. The contribution of this study not only enhances agricultural productivity but also mitigates environmental degradation associated with conventional farming practices. By synthesizing insights from environmental science, agriculture, and machine learning, this study not only contributes to the growing field of interdisciplinary research but also offers practical solutions to urgent global challenges at the intersection of sustainability and technology. Finally, we identify emerging trends and future research directions in this field, emphasizing the importance of interdisciplinary collaboration and the integration of domain expertise with ML methodologies to address complex environmental challenges effectively.  \niii  \nAcknowledgements  \nI extend my heartfelt gratitude to my advisor, Dr. Xin ","cbCaijafyc1ulnXp","https://ap.wps.com/l/cbCaijafyc1ulnXp","pdf",14499297,1,111,"English","en",105,"# Introduction\n## Research aims and motivation\n## Scope and problem context\n# Related Work\n## ML for environmental sustainability\n## Ecological prediction and optimization\n# Methodology\n## Data and feature engineering\n## Model training and evaluation\n# Results and Discussion\n## Model performance comparisons\n## Strengths, limitations, and insights\n# Conclusion and Future Work\n## Emerging trends and directions","[{\"question\":\"What does the dissertation propose for environmental sustainability?\",\"answer\":\"It proposes a comprehensive, machine-learning based approach to address environmental sustainability challenges, including agricultural residue management and ecological prediction tasks.\"},{\"question\":\"Which prediction and management tasks are covered in the study?\",\"answer\":\"The research includes crop residue management, soil CO2 flux prediction, and forest carbon system prediction to support sustainable development.\"},{\"question\":\"Which machine learning models are analyzed and compared?\",\"answer\":\"The study analyzes random forests, support vector machines, and ensemble learning techniques, discussing their strengths and limitations for the environmental tasks.\"}]","Machine Learning for Environmental Sustainability | 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