[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121592-en":3,"doc-seo-121592-105":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},121592,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems","Seawater intrusion management in coastal aquifers is investigated through performance testing of lower-fidelity models against a variable density high-fidelity benchmark. A pumping-optimization framework determines optimal pumping rates for the benchmark, then compares physics-based sharp-interface variants and machine-learning assisted sharp-interface corrections. A Random Forest approach learns a spatially adaptive correction factor that increases accuracy while retaining lower-fidelity efficiency. Both the original and corrected models are evaluated within a single-fidelity optimization routine, showing machine-learning assisted estimates closely approximate variable-density solutions and can support multi-fidelity groundwater management.","TYPE Original Research PUBLISHED 30 June 2023  \nDOI 10. 3389/frwa.2023.1195029  \nOPEN ACCESS  \nEDITED BY  \nThomas Graf,  \nLeibniz University Hannover, Germany  \nREVIEWED BY  \nMarwan Fahs,  \nNational School for Water and Environmental Engineering, France  \nSalim Heddam,  \nUniversity of Skikda, Algeria  \n*CORRESPONDENCE  \nGeorge Kopsiaftis  \n [gkopsiaf@survey.ntua.gr](gkopsiaf@survey.ntua.gr)  \nRECEIVED 27 March 2023  \nACCEPTED 22 May 2023  \nPUBLISHED 30 June 2023  \nCITATION  \nKopsiaftis G, Kaselimi M, Protopapadakis E, Voulodimos A, Doulamis A, Doulamis N and Mantoglou A (2023) Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems.  \nFront. Water 5:1195029 .  \ndoi: 10.3389/frwa.2023.1195029  \nCOPYRIGHT  \n© 2023 Kopsiaftis, Kaselimi, Protopapadakis, Voulodimos, Doulamis, Doulamis and Mantoglou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPerformance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems  \nGeorge Kopsiaftis1*, Maria Kaselimi2 , Eftychios Protopapadakis3 , Athanasios Voulodimos4 , Anastasios Doulamis2 ,  \nNikolaos Doulamis2 and Aristotelis Mantoglou1  \n1 Laboratory of Reclamation Works and Water Resources Management, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Athens, Greece, 2 Laboratory of Photogrammetry, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Athens, Greece, 3 School of Information Sciences, Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece, 4Artificial Intelligence and Learning Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece  \nIn this work we investigate the performance of various lower-fidelity models of seawater intrusion in coastal aquifer management problems. The variable density model is considered as the high-fidelity model and a pumping optimization framework is applied on a hypothetical coastal aquifer system in order to calculate the optimal pumping rates which are used as a benchmark for the lower-fidelity approaches. The examined lower-fidelity models could be classified in two categories: (1) physics-based models, which include several widely used variations of the sharp-interface approximation and (2) machine learning assisted models, which aim to improve the e􀀈ciency of the SI approach. The Random Forest method was utilized to create a spatially adaptive correction factor for the original sharp-interface model, which improves its accuracy without compromising its e􀀈ciency as a lower-fidelity model. Both the original sharp-interface and Machine Learning assisted model are then tested in a single-fidelity optimization method. The optimal pumping rated which were calculated using the Machine Learning based SI model su􀀈ciently approximate the solution from the variable density model. The Machine Learning assisted approximation seems to be a promising surrogate for the high-fidelity, variable density model and could be utilized in multi-fidelity groundwater management frameworks.  \nKEYWORDS  \nvariable density, sharp interface, machine learning, coastal aquifer, pumping optimization, random forests  \n1. Introduction  \nSeawater intrusion (SWI) models based on density-dependent approach constitute an accurate and realistic emulation of the saltwater/freshwater interaction and movement in coastal aquifers, since they incorporate several compone","cbCaivz9VGp9LILB","https://ap.wps.com/l/cbCaivz9VGp9LILB","pdf",1506571,1,14,"English","en",105,"# Introduction\n## Seawater intrusion modeling and variable-density cost\n## Surrogate and lower-fidelity model concepts","[{\"question\":\"What is the benchmark model in this study?\",\"answer\":\"The variable density model is used as the high-fidelity benchmark for coastal aquifer seawater intrusion management.\"},{\"question\":\"How are the lower-fidelity models categorized?\",\"answer\":\"They are grouped into physics-based sharp-interface models and machine learning assisted models that improve the sharp-interface approach.\"},{\"question\":\"What role does the Random Forest method play?\",\"answer\":\"Random Forest is used to build a spatially adaptive correction factor for the original sharp-interface model, improving accuracy without losing lower-fidelity efficiency.\"}]","Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems | 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is the benchmark model in this study?","Question",{"text":75,"@type":76},"The variable density model is used as the high-fidelity benchmark for coastal aquifer seawater intrusion management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the lower-fidelity models categorized?",{"text":80,"@type":76},"They are grouped into physics-based sharp-interface models and machine learning assisted models that improve the sharp-interface approach.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does the Random Forest method play?",{"text":84,"@type":76},"Random Forest is used to build a spatially adaptive correction factor for the original sharp-interface model, improving accuracy without losing lower-fidelity 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