Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean
The distribution of any non-conservative variable in the deep open ocean results from the circulation and mixing of water masses (WMs) of contrasting origin and from the initial preformed composition, modified during ongoing simultaneous biological and/or geochemical processes. Estimating the contri...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.904492/full |
| _version_ | 1828107325136699392 |
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| author | Cristina Romera-Castillo Jónathan Heras Marta Álvarez X. Antón Álvarez-Salgado Gadea Mata Eduardo Sáenz-de-Cabezón |
| author_facet | Cristina Romera-Castillo Jónathan Heras Marta Álvarez X. Antón Álvarez-Salgado Gadea Mata Eduardo Sáenz-de-Cabezón |
| author_sort | Cristina Romera-Castillo |
| collection | DOAJ |
| description | The distribution of any non-conservative variable in the deep open ocean results from the circulation and mixing of water masses (WMs) of contrasting origin and from the initial preformed composition, modified during ongoing simultaneous biological and/or geochemical processes. Estimating the contribution of the WMs composing a sample is useful to trace the distribution of each water mass and to quantitatively separate the physical (mixing) and biogeochemical components of the variability of any, non- conservative variable (e.g., dissolved organic carbon, prokaryote biomass) in the ocean. Other than potential temperature and salinity, additional semi-conservative and non-conservative variables have been used to solve the mixing of more than three water masses using Optimum Multi-Parameter (OMP) approaches. Successful application of an OMP analysis requires knowledge of the characteristics of the water masses in their source regions as well as their circulation and mixing patterns. Here, we propose the application of multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). Our model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Therefore, basic hydrographic data collected during typical research cruises or autonomous systems can be used as input variables and provide results in real time. The model can be fed with new solutions from compatible OMP analyses as well as with new water masses not previously considered in it. Our tool will provide knowledge on water mass composition and distribution to a broader community of marine scientists not specialized in OMP analysis and/or in the oceanography of the studied area. This will allow a quantitative analysis of the effect of water mass mixing on the variables or processes under study. |
| first_indexed | 2024-04-11T10:29:25Z |
| format | Article |
| id | doaj.art-88722d0e9e5d462aa030fba16d927065 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-04-11T10:29:25Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-88722d0e9e5d462aa030fba16d9270652022-12-22T04:29:29ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-10-01910.3389/fmars.2022.904492904492Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic OceanCristina Romera-Castillo0Jónathan Heras1Marta Álvarez2X. Antón Álvarez-Salgado3Gadea Mata4Eduardo Sáenz-de-Cabezón5Institut de Ciències del Mar-Consejo Superior de Investigaciones Científicas (CSIC), Passeig Marítim de la Barceloneta, Barcelona, SpainUniversidad de La Rioja, Logroño, SpainCentro Nacional Instituto Español de Oceanografía, Consejo Superior de Investigaciones Científicas (CSIC), A Coruña, SpainInstituto de Investigacións Mariñas-Consejo Superior de Investigaciones Científicas (CSIC), Pontevedra, SpainUniversidad de La Rioja, Logroño, SpainUniversidad de La Rioja, Logroño, SpainThe distribution of any non-conservative variable in the deep open ocean results from the circulation and mixing of water masses (WMs) of contrasting origin and from the initial preformed composition, modified during ongoing simultaneous biological and/or geochemical processes. Estimating the contribution of the WMs composing a sample is useful to trace the distribution of each water mass and to quantitatively separate the physical (mixing) and biogeochemical components of the variability of any, non- conservative variable (e.g., dissolved organic carbon, prokaryote biomass) in the ocean. Other than potential temperature and salinity, additional semi-conservative and non-conservative variables have been used to solve the mixing of more than three water masses using Optimum Multi-Parameter (OMP) approaches. Successful application of an OMP analysis requires knowledge of the characteristics of the water masses in their source regions as well as their circulation and mixing patterns. Here, we propose the application of multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). Our model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Therefore, basic hydrographic data collected during typical research cruises or autonomous systems can be used as input variables and provide results in real time. The model can be fed with new solutions from compatible OMP analyses as well as with new water masses not previously considered in it. Our tool will provide knowledge on water mass composition and distribution to a broader community of marine scientists not specialized in OMP analysis and/or in the oceanography of the studied area. This will allow a quantitative analysis of the effect of water mass mixing on the variables or processes under study.https://www.frontiersin.org/articles/10.3389/fmars.2022.904492/fullmachine learningextremely randomized treesoptimum multi-parameter analysiswater mass mixingAtlantic Ocean |
| spellingShingle | Cristina Romera-Castillo Jónathan Heras Marta Álvarez X. Antón Álvarez-Salgado Gadea Mata Eduardo Sáenz-de-Cabezón Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean Frontiers in Marine Science machine learning extremely randomized trees optimum multi-parameter analysis water mass mixing Atlantic Ocean |
| title | Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean |
| title_full | Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean |
| title_fullStr | Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean |
| title_full_unstemmed | Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean |
| title_short | Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean |
| title_sort | application of multi regression machine learning algorithms to solve ocean water mass mixing in the atlantic ocean |
| topic | machine learning extremely randomized trees optimum multi-parameter analysis water mass mixing Atlantic Ocean |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.904492/full |
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