Ensemble machine learning model prediction and metaheuristic optimisation of oil spills using organic absorbents : supporting sustainable maritime

Dung Le, Quang; Pham, Duc; Bui, Thi Anh Em; Nquyen, Lan Huong; Nguyen, Phuoc Quy Phong; Dang, Thanh Nam

doi:10.2478/pomr-2025-0029

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Tytuł artykułu

Ensemble machine learning model prediction and metaheuristic optimisation of oil spills using organic absorbents : supporting sustainable maritime

Autorzy

Dung Le Quang , Pham Duc , Bui Thi Anh Em , Nquyen Lan Huong , Nguyen Phuoc Quy Phong , Dang Thanh Nam

Treść / Zawartość

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DOI

10.2478/pomr-2025-0029

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Języki publikacji

Abstrakty

The study discusses the urgent necessity of environmentally friendly remedies to tackle the increasing frequency of oil spills. Conventional remedial oil leak techniques, such as mechanical recovery and chemical dispersants, may cause environmental damage and suffer from low effectiveness. Organic absorbents are environment-friendly and low-cost substitutes; however, their acceptance is hampered by inadequate performance and optimisation studies. To close this gap, our work combines metaheuristic algorithms with ensemble machine learning and suggests a hybrid technique for the precise prediction and improvement of oil removal efficiency. Using Random Forest (RF) and XGBoost models, high R2 values (RF: 0.9517–0.9559; XGBoost: 0.9760), minimal errors, and strong generalisation were obtained by predictive modelling. Operating conditions were optimised using Grey Wolf Optimisation (GWO), showing an optimal percentage of oil removed (POR) of 93.59%. Combining metaheuristics with machine learning ensures accuracy and practical results, tackling the complexity of oil spill control. By concentrating on organic absorbents, the study fits with worldwide sustainability initiatives and provides a useful foundation for actual implementation.

Słowa kluczowe

metaheuristics oil spills organic absorbents machine learning ensemble methods

Wydawca

Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology

Czasopismo

Polish Maritime Research

Rocznik

2025

Tom

nr 2

Strony

141--155

Opis fizyczny

Bibliogr. 70 poz., rys., tab.

Twórcy

autor

Dung Le Quang

Laboratory of Ecology and Environmental Management, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City
Faculty of Applied Technology, School of Technology, Van Lang University, Ho Chi Minh City, Viet Nam

https://orcid.org/0000-0002-5951-6816

autor

Pham Duc

Faculty of Engineering, Dong Nai Technology University, Bien Hoa City, Viet Nam

autor

Bui Thi Anh Em

bta.em@hutech.edu.vn

Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam

autor

Nquyen Lan Huong

Institute of Mechanical Engineering, Vietnam Maritime University, Haiphong, Viet Nam

https://orcid.org/0009-0007-7977-4007

autor

Nguyen Phuoc Quy Phong

Maritime College II, Ho Chi Minh City, Viet Nam

autor

Dang Thanh Nam

Institute of Maritime, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam

https://orcid.org/0009-0002-3281-1927

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).

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Bibliografia

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