دورية أكاديمية
أعرض في EDS

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

التفاصيل البيبلوغرافية
العنوان: Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.
المؤلفون: Adhikary S; Spiraldevs Automation Industries Pvt. Ltd., Raiganj, West Bengal, India., Tiwari SP; King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia., Banerjee S; Wingbiotics, Baghajatin, Kolkata, West Bengal, India., Dwivedi AD; Cybersecurity Section, Aalborg University, Copenhagen, Denmark., Rahman SM; King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.
المصدر: PeerJ [PeerJ] 2024 May 08; Vol. 12, pp. e17361. Date of Electronic Publication: 2024 May 08 (Print Publication: 2024).
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: PeerJ Inc Country of Publication: United States NLM ID: 101603425 Publication Model: eCollection Cited Medium: Internet ISSN: 2167-8359 (Electronic) Linking ISSN: 21678359 NLM ISO Abbreviation: PeerJ Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Corte Madera, CA : PeerJ Inc.
مواضيع طبية MeSH: Phytoplankton* , Remote Sensing Technology*/methods , Remote Sensing Technology*/instrumentation , Machine Learning*, Oceans and Seas ; Environmental Monitoring/methods ; Supervised Machine Learning
مستخلص: Phytoplankton 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 (R 2 ) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts.
Competing Interests: Subhrangshu Adhikary is employed by Spiraldevs Automation Industries Pvt. Ltd. and Saikat Banerjee is employed by Wingbiotics.
(© 2024 Adhikary et al.)
References: Environ Sci Technol. 2020 May 5;54(9):5569-5579. (PMID: 32292033)
Sensors (Basel). 2019 Oct 03;19(19):. (PMID: 31623312)
Remote Sens Environ. 2019 Aug;229:32-47. (PMID: 31379395)
Sci Rep. 2021 Jan 13;11(1):953. (PMID: 33441617)
Nat Commun. 2021 Nov 17;12(1):6634. (PMID: 34789722)
Nat Commun. 2021 Feb 22;12(1):1211. (PMID: 33619262)
Sci Total Environ. 2022 Oct 20;844:157191. (PMID: 35810889)
Sci Total Environ. 2021 Jun 1;771:144811. (PMID: 33545474)
Environ Pollut. 2021 Oct 1;286:117489. (PMID: 34119860)
Environ Sci Pollut Res Int. 2021 May;28(20):25830-25843. (PMID: 33474667)
Sci Total Environ. 2021 Oct 10;790:148086. (PMID: 34098270)
Environ Monit Assess. 2023 Jun 1;195(6):788. (PMID: 37261624)
Sci Total Environ. 2022 Jan 10;803:149805. (PMID: 34492494)
Front Mar Sci. 2019 Aug 29;6:1-30. (PMID: 36817748)
Sci Total Environ. 2021 Jun 1;771:145167. (PMID: 33736151)
فهرسة مساهمة: Keywords: Global waters; Machine learning; Ocean biogeochemistry; Phytoplankton; Regression
تواريخ الأحداث: Date Created: 20240513 Date Completed: 20240513 Latest Revision: 20240514
رمز التحديث: 20240514
مُعرف محوري في PubMed: PMC11088370
DOI: 10.7717/peerj.17361
PMID: 38737741
قاعدة البيانات: MEDLINE
الوصف
تدمد:2167-8359
DOI:10.7717/peerj.17361