Integrated Machine Learning and Multivariate Analyses to Apportion the Different Organic Matter Sources to the Coastal Sediment Sink: A Case Study in Organic Geochemistry

Integrated Machine Learning and Multivariate Analyses to Apportion the Different Organic Matter Sources to the Coastal Sediment Sink: A Case Study in Organic Geochemistry | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 February 2025 V1 Latest version Share on Integrated Machine Learning and Multivariate Analyses to Apportion the Different Organic Matter Sources to the Coastal Sediment Sink: A Case Study in Organic Geochemistry Authors : Yeganeh Mirzaei 0000-0003-2317-1841 [email protected] , Cameron Skinner 0009-0009-3721-1892 , and Yves Gélinas 0000-0001-5751-8378 Authors Info & Affiliations https://doi.org/10.22541/au.173921696.65840916/v1 228 views 145 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Understanding the key parameters in apportioning the contribution of organic matter (OM) sources in diverse settings, such as costal sediments, is critical due to its implications for carbon cycling, Earth’s archival record, ecosystem dynamics, and land-sea interactions. This study applies integrated machine learning (ML) and multivariant analyses to overcome the limitations of traditional single-variable proxies in OM source apportionment, focusing on the natural laboratory of the St. Lawrence Estuary and Gulf, the largest semi-enclosed estuarine system on Earth. Using principal component analysis (PCA), random forest (RF), and partial least squares regression (PLSR), we investigate the complex relationships between biomarkers distributions (C17-C27 n-alkanes), elemental concentrations (C and N), and both bulk and compound specific δ13C isotope values. While PCA effectively helps visualizing compositional differences, its predictive capability for continuous estimates of the contribution from each OM fraction was limited. In contrast, supervised regression models, particularly PLSR, excelled at quantifying the proportional contributions of OM sources in regions of high collinearity and linear mixing. Our results reveal a gradient of increasing marine OM dominance along the estuarine continuum, driven by reduced terrestrial inputs and elevated marine productivity. This study highlights the effectiveness of ML-based multivariate approaches in improving source characterization and feature selection, enhancing our understanding of organic carbon dynamics in challenging sedimentary environments. Supplementary Material File (1019905_0_merged_1737229554.pdf) Download 1.21 MB File (mirzaei_sleg_modeling_jan18.docx) Download 1.53 MB File (supplementary materials.docx) Download 865.25 KB Information & Authors Information Version history V1 Version 1 10 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords machine learning marine sediments organic biogeochemistry organic matter origins prediction and classification models stable isotopes and molecular tracers Authors Affiliations Yeganeh Mirzaei 0000-0003-2317-1841 [email protected] Concordia University - Loyola Campus View all articles by this author Cameron Skinner 0009-0009-3721-1892 Concordia University - Loyola Campus View all articles by this author Yves Gélinas 0000-0001-5751-8378 GEOTOP and Concordia University View all articles by this author Metrics & Citations Metrics Article Usage 228 views 145 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yeganeh Mirzaei, Cameron Skinner, Yves Gélinas. Integrated Machine Learning and Multivariate Analyses to Apportion the Different Organic Matter Sources to the Coastal Sediment Sink: A Case Study in Organic Geochemistry. Authorea . 10 February 2025. DOI: https://doi.org/10.22541/au.173921696.65840916/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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