[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127187-en":3,"doc-seo-127187-105":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},127187,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing","Phytoplankton drive major oxygen production in oceans, seas, and large water bodies, yet their growth can be limited by imbalances in biogeochemical factors such as salinity, pH, and mineral availability. To address this gap, the study uses supervised regression models on reanalysis data to estimate global phytoplankton levels across global waters. Multiple machine learning regressors—random forest, extra trees, bagging, and histogram-based gradient boosting—are trained on data from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. The best results reach an R2 of up to 0.96, enabling deployment after further validation on larger datasets to support and complement in-situ measurements.","Aalborg Universitet  \nGlobal marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing  \nAdhikary, Subhrangshu; Tiwari, Surya Prakash; Banerjee, Saikat; Dwivedi, Ashutosh Dhar; Rahman, Syed Masiur  \nPublished in: PeerJ  \nDOI (link to publication from Publisher):  \n10.7717/PEERJ.17361  \nCreative Commons License  \nCC BY 4.0  \nPublication date: 2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nAdhikary, S. , Tiwari, S. P. , Banerjee, S. , Dwivedi, A. D. , & Rahman, S. M. (2024) . Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing. PeerJ, 12 , Article e17361 . [https://doi.org/10.7717/PEERJ.17361](https://doi.org/10.7717/PEERJ.17361)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \n1 2  \n3 4  \nSubmitted 19 September 2023  \nAccepted 18 April 2024  \nPublished 8 May 2024  \nCorresponding author Ashutosh Dhar Dwivedi, [addw@es.aau.dk](addw@es.aau.dk)  \nAcademic editor Diogo Provete  \nAdditional Information and Declarations can be found on page 20  \nDOI 10.7717/peerj.17361  \nCopyright 2024 Adhikary et al.  \nDistributed under  \nCreative Commons CC-BY 4.0  \nGlobal marine phytoplankton dynamics analysis with machine learning andreanalyzed remote sensing  \nSubhrangshu Adhikary1, Surya Prakash Tiwari2, Saikat Banerjee3, Ashutosh Dhar Dwivedi4 and Syed Masiur Rahman2  \nSpiraldevs Automation Industries Pvt. Ltd., Raiganj, West Bengal, India  \nKing Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia Wingbiotics, Baghajatin, Kolkata, West Bengal, India  \nCybersecurity Section, Aalborg University, Copenhagen, Denmark  \nABSTRACT  \nPhytoplankton are the world’s largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R2) of up to 0.96 . After further validation with larger datasets, the model can be deployed ina production environment in an attempt to complement in-situ measurement efforts.  \nSubjects Data Mining and Machine Learning, Data Science  \nKeywords Phytoplankton, Machine learning, Ocean biogeochemistry, Global waters, Regression INTRODUCTION  \nPhytoplankton are microscopic, single-celled photosynthetic organisms that live in both fresh and salty water environments. They","cbCailAfuv2ENzSN","https://ap.wps.com/l/cbCailAfuv2ENzSN","pdf",13075445,1,26,"English","en",105,"# Abstract\n# Introduction\n## Phytoplankton role in marine ecosystems\n## Biogeochemical controls on growth\n## Remote sensing and reanalysis data for estimation","[{\"question\":\"Why is phytoplankton important for marine ecosystems?\",\"answer\":\"Phytoplankton are microscopic photosynthetic organisms that contribute significantly to global oxygen production and form a major part of the marine food chain, helping maintain marine ecological balance.\"},{\"question\":\"How does the study estimate global phytoplankton levels?\",\"answer\":\"It applies supervised regression machine learning methods to reanalysis data, targeting phytoplankton levels in global waters using the Copernicus Global Ocean Biogeochemistry Hindcast dataset.\"},{\"question\":\"Which machine learning models are evaluated, and how accurate are the results?\",\"answer\":\"Random forest, extra trees, bagging, and histogram-based gradient boosting regression are tested, achieving predictions with a coefficient of determination (R2) up to 0.96.\"}]","Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing | PDF",1785937396,66,{"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},"global-marine-phytoplankton-dynamics-analysis-with-machine-learning-and-reanalyzed-remote-sensing","",{"@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/global-marine-phytoplankton-dynamics-analysis-with-machine-learning-and-reanalyzed-remote-sensing/127187/",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},"Why is phytoplankton important for marine ecosystems?","Question",{"text":75,"@type":76},"Phytoplankton are microscopic photosynthetic organisms that contribute significantly to global oxygen production and form a major part of the marine food chain, helping maintain marine ecological balance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study estimate global phytoplankton levels?",{"text":80,"@type":76},"It applies supervised regression machine learning methods to reanalysis data, targeting phytoplankton levels in global waters using the Copernicus Global Ocean Biogeochemistry Hindcast dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are evaluated, and how accurate are the results?",{"text":84,"@type":76},"Random forest, extra trees, bagging, and histogram-based gradient boosting regression are tested, achieving predictions with a coefficient of determination (R2) up to 0.96.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]