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dc.contributor.authorUddin, MD. Galal
dc.contributor.authorNash, Stephen
dc.contributor.authorRahman, Azizur
dc.contributor.authorOlbert, Agnieszka
dc.contributor.authorDabrowski, Tomasz
dc.date.accessioned2024-08-26T16:14:01Z
dc.date.available2024-08-26T16:14:01Z
dc.date.issued2024-02
dc.identifier.citationUddin, M. G., Nash, S., Rahman, A., Dabrowski, T., & Olbert, A. I. (2024). Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches. Environmental Research, 242, 117755.en_US
dc.identifier.issn0013-9351
dc.identifier.urihttp://hdl.handle.net/10793/1972
dc.description0013-9351/© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). https://doi.org/10.1016/j.envres.2023.117755en_US
dc.description.abstractAssessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, and complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) and artificial intelligence (AI), enhancing the reliability and accuracy of trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for transitional and coastal (TrC) waters, with a focus on Cork Harbour, Ireland, as a case study. Our innovative approach, named the Assessment Trophic Status Index (ATSI) model, comprises three main components: the selection of pertinent water quality indicators, the computation of ATSI scores, and the implementation of a new classification scheme. To optimize input data and minimize redundancy, we employed ML techniques, including advanced deep learning methods. Specifically, we developed a CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE = 0.0, MAE = 0.01) and testing (RMSE = 0.0, MSE = 0.0, MAE = 0.01) phases. Utilizing a novel linear rescaling interpolation function, we calculated ATSI scores and evaluated the model's sensitivity and efficiency across diverse application domains, employing metrics such as R2, the Nash-Sutcliffe efficiency (NSE), and the model efficiency factor (MEF). The results consistently revealed heightened sensitivity and efficiency across all application domains. Additionally, we introduced a brand new classification scheme for ranking the trophic status of transitional and coastal waters. To assess spatial sensitivity, we applied the ATSI model to four distinct waterbodies in Ireland, comparing trophic assessment outcomes with the Assessment of Trophic Status of Estuaries and Bays in Ireland (ATSEBI) System. Remarkably, significant disparities between the ATSI and ATSEBI System were evident in all domains, except for Mulroy Bay. Overall, our research significantly enhances the accuracy of trophic status assessments in marine ecosystems. The ATSI model, combined with cutting-edge ML techniques and our new classification scheme, represents a promising avenue for evaluating and monitoring trophic conditions in TrC waters. The study also demonstrated the effectiveness of ATSI in assessing trophic status across various waterbodies, including lakes, rivers, and more. These findings make substantial contributions to the field of marine ecosystem management and conservation.en_US
dc.description.sponsorshipHardiman Research Scholarship of the University of Galway, Irelanden_US
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.relation.ispartofseriesEnvironmental Research;242 (117755)
dc.subjectCoastal and transitional watersen_US
dc.subjectML and AI approachen_US
dc.subjectATSI modelen_US
dc.subjectTrophic status assessmenten_US
dc.subjectCork Harbouren_US
dc.titleData-Driven Modelling for Assessing Trophic Status in Marine Ecosystems Using Machine Learning Approachesen_US
dc.typeArticleen_US
dc.identifier.doi10.2139/ssrn.4516226
refterms.dateFOA2024-08-26T16:14:03Z


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