[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122021-en":3,"doc-seo-122021-105":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":20,"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},122021,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Harnessing Surrogate Modeling and Machine Learning for Management of Water Resources and Environmental Systems","Increasing frequency of climate extremes, population growth, and constrained infrastructure expansion make efficient and resilient water and environmental planning urgently necessary. At the same time, high-dimensional data and the need for rapid, informed decisions introduce major operational challenges. This dissertation develops integrated, accelerated simulation, optimization, and decision-support models driven by machine learning and surrogate modeling. Four chapters address decision-making support, economic evaluation with watershed and reservoir models, hydrological prediction in gauged and ungauged basins, and unsupervised analytics for quantifying engineered nanoparticle contamination.","University of South Carolina  \nScholar Commons  \nTheses and Dissertations  \nFall 2023  \nHarnessing Surrogate Modeling and Machine Learning for Management of Water Resources and Environmental Systems Mahdi Erfani  \nFollow this and additional works at: [https://scholarcommons.sc.edu/etd](https://scholarcommons.sc.edu/etd)  \n Part of the Civil and Environmental Engineering Commons  \nRecommended Citation  \nErfani, M. (2023) . Harnessing Surrogate Modeling and Machine Learning for Management of Water Resources and Environmental Systems. (Doctoral dissertation) . Retrieved from  \n[https://scholarcommons.sc.edu/etd/7540](https://scholarcommons.sc.edu/etd/7540)  \nThis Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please [contact digres@mailbox.sc.edu](contact digres@mailbox.sc.edu).  \nHarnessing Surrogate Modeling and Machine Learning for Management of Water Resources and Environmental Systems  \nby  \nMahdi Erfani  \nBachelor of Science  \nIran University of Science and Technology 2015  \nMaster of Science  \nIran University of Science and Technology 2018  \nSubmitted in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy in Civil Engineering College of Engineering and Computing University of South Carolina 2023  \nAccepted by: Erfan Goharian, Major Professor Hanif Chaudhry, Committee Member Jasim Imran, Committee Member Josué Medellín-Azuara, Committee Member  \nAnn Vail, Dean of the Graduate School  \nCopyright by Mahdi Erfani, 2023 All Rights Reserved.  \nAbstract  \nIn a world characterized by increasing and more frequent climate extreme events, population growth, and limited opportunities for infrastructural expansion, the imperative for more efficient and resilient planning, design, and operation of water resources and environmental systems has never been more apparent. Moreover, the emergence of highdimensional data and the demand for quick and informed decision-making have created a critical challenge for the operation and management of these systems. To address these issues effectively, the ability to rapidly grasp and accelerate the analysis of this data is paramount, and this necessitates the innovative use of machine learning and surrogate modeling techniques, enabling timely, data-driven decisions in the face of complex water and environmental problems. To address these multifaceted challenges and provide valuable insights to managers and operators, the development of integrated and accelerated water resources simulation, optimization, and decision support models is critical. Innovative modeling approaches, such as machine learning, offer an ideal alternative to complex numerical models and enable the extraction of vital information from the wealth of high-dimensional data. This dissertation is structured around three chapters, each dedicated to a specific application of machine learning and surrogate modeling for the improved and informed management of Water Resources and Environmental Systems, including optimized decision-making, modeling and forecast, and classification and data analytics.  \nThe first chapter focuses on the development of surrogate simulation-optimization models as decision support tools for managing integrated water resources systems. These systems often feature non-linear processes and decision spaces for conflicting objectives constrained by convex functions, demanding extensive search, computational time, and hardware resources. The second chapter emphasizes the creation of surrogate economic evaluation components to integrate with watershed and reservoir models. This framework is then applied to assess the feasibility of incorporating Flood Managed Aquifer Recharge (Flood-MAR) into the operation of Folsom Reservoir and American River Watershed management in California. Chapter three pivots to the enhancement of hydrological modelin","cbCaifXARyv3zIV7","https://ap.wps.com/l/cbCaifXARyv3zIV7","pdf",4843201,1,157,"English","en",105,"# Abstract\n# Table of Contents\n## Chapter 1: Introduction\n## Chapter 2: Developing Decision Support Tools for Integrated Watershed Management Using Machine Learning and Policy Search\n## Chapter 3\n## Chapter 4","[{\"question\":\"Why are machine learning and surrogate modeling needed for water resources and environmental management?\",\"answer\":\"The work targets the need for faster, data-driven analysis and decision-making under complex, non-linear processes and high-dimensional data challenges.\"},{\"question\":\"What does Chapter 1 focus on in the dissertation?\",\"answer\":\"Chapter 1 develops surrogate simulation-optimization models that act as decision-support tools for managing integrated water resources systems.\"},{\"question\":\"How does the dissertation extend machine learning to prediction and data analysis?\",\"answer\":\"It enhances hydrological modeling for both gauged and ungauged basins, and uses unsupervised machine learning to quantify contamination from engineered nanoparticles via nonlinear feature reduction and transformation.\"}]","Harnessing Surrogate Modeling and Machine Learning for Management of Water Resources and Environmental Systems | 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