[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119526-en":3,"doc-seo-119526-105":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},119526,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Environmental impact assessment of ocean energy converters using quantum machine learning - Promising quantum ML for ocean EIA","The depletion of fossil energy reserves and the pollution from fossil fuels make ocean renewable energy increasingly important, including wave and tidal power. Large-scale ocean energy converter deployments can nevertheless harm marine ecosystems. Environmental impact assessment also depends on the large and diverse datasets produced by ocean monitoring methods. This review analyzes environmental impacts using machine learning and quantum machine learning, showing quantum approaches improve calculation accuracy versus classical methods, enabling more effective EIA in complex ocean environments.","Journal of Environmental Management 362 (2024) 121275  \nContents lists available at ScienceDirect  \nJournal of Environmental Management  \njournal [homepage: www.elsevier.com/locate/jenvman](homepage: www.elsevier.com/locate/jenvman)  \n| Review\u003Cbr>Environmental impact assessment of ocean energy converters using quantum machine learning\u003Cbr>*\u003Cbr>Taha Rezaei , Akbar Javadi\u003Cbr>Department of Engineering, University of Exeter, EX4 4QJ, United Kingdom |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Handling editor: Dr. Lixiao Zhang |  | The depletion of fossil energy reserves and the environmental pollution caused by these sources highlight the need to harness renewable energy sources from the oceans, such as waves and tides, due to their high potential. On the other hand, the large-scale deployment of ocean energy converters to meet future energy needs requires the use of large farms of these converters, which may have negative environmental impacts on the ocean ecosystem. In the meantime, a very important point is the volume of data produced by different methods of collecting data from the ocean for their analysis, which makes the use of advanced tools such as different machine learning algorithms even more colorful. In this article, some environmental impacts of ocean energy devices have been analyzed using machine learning and quantum machine learning. The results show that quantum machine learning performs better than its classical counterpart in terms of calculation accuracy. This approach offers a promising new method for environmental impact assessment, especially in a complex environment such as the ocean. |  |\n| Keywords:\u003Cbr>Ocean energy converters\u003Cbr>Marine Environment\u003Cbr>Marine ecosystem\u003Cbr>Environmental impacts\u003Cbr>Machine learning\u003Cbr>Quantum machine learning |  |  |  |\n\n1. Introduction  \nWith the increasing developments in technology and technological infrastructure, there is a rising demand, globally, for electricity. Given the promonent role of fossil fuels in electricity generation and supply, there is an increasing concern over the environmental impacts of burning fossil fuels. Meanwhile, the role of renewable energy is relatively small and needs more research and development (Farrok et al., 2020). With the global movement to reduce greenhouse gas emissions by 2050, countries are expected to ramp up their efforts towards developing renewable energy resources. This is one of the most important areas discussed by the United Nations with the introduction of“carbon neutrality by 2050\". To achieve this goal, many countries are committed to greatly reducing greenhouse gas emissions (Guo and Ringwood, 2021). For example, European countries plan to replace 32% of their energy demand with energy from renewable sources by 2030 (Galparsoro et al., 2021). There has been a significant increase in the production of renewable energy, which promises to accelerate the commercial production of this energy, especially in North America, the United Kingdom, Europe, and China. Among the different types of renewable energy systems, ocean energy, especially tidal energy, has received significant financial support for research and development  \n(‘REN21’ and 2021, 2021). One of the main goals of the development of renewable energy is to decarbonize and help reduce climate change. In addition, the development of renewable energy infrastructure to generate electricity for remote areas and places with limited access to energy requires the use of high-efficiency and reliable source of energy, and ocean energy meets this requirement (Galparsoro et al., 2021). In this regard, the use of machine learning methods, which are powerful tools for environmental assessments, especially in oceanic environments, is suggested. One of the key applications of machine learning algorithms is in the initial data analysis stage, where they manage and analyze large volumes of collected data and prepare it for further evaluatio","cbCaifRXxISjaDSm","https://ap.wps.com/l/cbCaifRXxISjaDSm","pdf",4771290,1,10,"English","en",105,"# Introduction\n# Overview of types of ocean power converters","[{\"question\":\"Why is ocean energy considered important in the environmental context?\",\"answer\":\"Ocean energy is highlighted as a renewable alternative as fossil fuel reserves deplete and their use causes environmental pollution. Its high potential supports future electricity generation while reducing reliance on fossil sources.\"},{\"question\":\"What environmental challenge arises from deploying ocean energy converters at scale?\",\"answer\":\"Large farms of ocean energy converters may generate negative environmental impacts on the ocean ecosystem, requiring careful assessment before and during deployment.\"},{\"question\":\"How does quantum machine learning improve environmental impact assessment compared with classical machine learning?\",\"answer\":\"The review reports that quantum machine learning performs better than classical methods in calculation accuracy. This makes it a promising approach for environmental impact assessment in complex ocean conditions.\"}]","Environmental impact assessment of ocean energy converters using quantum machine learning - Promising quantum ML for ocean EIA | PDF",1785724780,25,{"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},"environmental-impact-assessment-of-ocean-energy-converters-using-quantum-machine-learning-promising-quantum-ml-for-ocean-eia","",{"@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/environmental-impact-assessment-of-ocean-energy-converters-using-quantum-machine-learning-promising-quantum-ml-for-ocean-eia/119526/",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","2026-08-03",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},"Why is ocean energy considered important in the environmental context?","Question",{"text":75,"@type":76},"Ocean energy is highlighted as a renewable alternative as fossil fuel reserves deplete and their use causes environmental pollution. Its high potential supports future electricity generation while reducing reliance on fossil sources.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What environmental challenge arises from deploying ocean energy converters at scale?",{"text":80,"@type":76},"Large farms of ocean energy converters may generate negative environmental impacts on the ocean ecosystem, requiring careful assessment before and during deployment.",{"name":82,"@type":73,"acceptedAnswer":83},"How does quantum machine learning improve environmental impact assessment compared with classical machine learning?",{"text":84,"@type":76},"The review reports that quantum machine learning performs better than classical methods in calculation accuracy. 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