Using XGBoost and memetic programming to identify hotspots of sediment plastic pollution

Despite growing global initiatives on sustainable plastic management, less than 10 % of plastic waste is effectively recycled, resulting in widespread environmental dispersion and pollution. This study examines the relative influence of topographic, hydrologic, and urban factors on the proliferation...

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Published in:	Environmental pollution (1987) Vol. 387; p. 127329
Main Authors:	Shiwomeh, Desmond N., Kantoush, Sameh A., Saber, Mohamed, Sumi, Tetsuya, Fantong, Wilson Y., Quang Nguyen, Binh, Abdrabo, Karim I., Mabrouk, Emad
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 15.12.2025
Subjects:	Algorithms Anthropogenic plastic hotspots Boosting Machine Learning Algorithms Cameroon Environmental Monitoring - methods Environmental Pollution - statistics & numerical data Geologic Sediments - chemistry Influence parameters Memetic programming algorithm Natural plastic hotspots Plastic debris dispersal Plastics - analysis Proliferation of plastic hotspots XGBoost model Cameroon Memetic programming algorithm Anthropogenic plastic hotspots Natural plastic hotspots Plastic debris dispersal Influence parameters XGBoost model Proliferation of plastic hotspots
ISSN:	0269-7491, 1873-6424, 1873-6424
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Abstract	Despite growing global initiatives on sustainable plastic management, less than 10 % of plastic waste is effectively recycled, resulting in widespread environmental dispersion and pollution. This study examines the relative influence of topographic, hydrologic, and urban factors on the proliferation of plastic hotspots (macroplastics) in the urbanized Mfoundi subbasin of Yaoundé, Cameroon. To achieve this, we employed Extreme Gradient Boosting (XGBoost) and Memetic Programming (MP) algorithms to classify both anthropogenic and naturally occurring plastic hotspots based on twelve spatially explicit parameters. This was then followed by assessing model performance through five key metrics: accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results reveal that topographic and hydrologic factors exert a stronger influence on hotspot formation than urban variables. Among the urban features, population density, road proximity, and waste management infrastructure were more strongly associated with anthropogenic hotspots, while land use exhibited limited influence overall. When multiple parameters were combined, model performance metrics were observed to improve significantly (≥75 % accuracy). The MP algorithm demonstrated more robust generalization across test datasets, whereas XGBoost exhibited signs of overfitting. These findings underscore the value of spatially explicit machine learning models for guiding targeted interventions in plastic pollution mitigation. [Display omitted] •Topographic features were the most influential drivers of natural plastic hotspots.•Combining multiple parameters enhanced classification accuracy above 75 %.•Memetic programming yielded more stable generalization than XGBoost.•Urban parameters had a stronger influence on anthropogenic than natural hotspots.
AbstractList	Despite growing global initiatives on sustainable plastic management, less than 10% of plastic waste is effectively recycled, resulting in widespread environmental dispersion and pollution. This study examines the relative influence of topographic, hydrologic, and urban factors on the proliferation of plastic hotspots (macroplastics) in the urbanized Mfoundi subbasin of Yaoundé, Cameroon. To achieve this, we employed Extreme Gradient Boosting (XGBoost) and Memetic Programming (MP) algorithms to classify both anthropogenic and naturally occurring plastic hotspots based on twelve spatially explicit parameters. This was then followed by assessing model performance through five key metrics: accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results reveal that topographic and hydrologic factors exert a stronger influence on hotspot formation than urban variables. Among the urban features, population density, road proximity, and waste management infrastructure were more strongly associated with anthropogenic hotspots, while land use exhibited limited influence overall. When multiple parameters were combined, model performance metrics were observed to improve significantly (≥75% accuracy). The MP algorithm demonstrated more robust generalization across test datasets, whereas XGBoost exhibited signs of overfitting. These findings underscore the value of spatially explicit machine learning models for guiding targeted interventions in plastic pollution mitigation.Despite growing global initiatives on sustainable plastic management, less than 10% of plastic waste is effectively recycled, resulting in widespread environmental dispersion and pollution. This study examines the relative influence of topographic, hydrologic, and urban factors on the proliferation of plastic hotspots (macroplastics) in the urbanized Mfoundi subbasin of Yaoundé, Cameroon. To achieve this, we employed Extreme Gradient Boosting (XGBoost) and Memetic Programming (MP) algorithms to classify both anthropogenic and naturally occurring plastic hotspots based on twelve spatially explicit parameters. This was then followed by assessing model performance through five key metrics: accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results reveal that topographic and hydrologic factors exert a stronger influence on hotspot formation than urban variables. Among the urban features, population density, road proximity, and waste management infrastructure were more strongly associated with anthropogenic hotspots, while land use exhibited limited influence overall. When multiple parameters were combined, model performance metrics were observed to improve significantly (≥75% accuracy). The MP algorithm demonstrated more robust generalization across test datasets, whereas XGBoost exhibited signs of overfitting. These findings underscore the value of spatially explicit machine learning models for guiding targeted interventions in plastic pollution mitigation. Despite growing global initiatives on sustainable plastic management, less than 10 % of plastic waste is effectively recycled, resulting in widespread environmental dispersion and pollution. This study examines the relative influence of topographic, hydrologic, and urban factors on the proliferation of plastic hotspots (macroplastics) in the urbanized Mfoundi subbasin of Yaoundé, Cameroon. To achieve this, we employed Extreme Gradient Boosting (XGBoost) and Memetic Programming (MP) algorithms to classify both anthropogenic and naturally occurring plastic hotspots based on twelve spatially explicit parameters. This was then followed by assessing model performance through five key metrics: accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results reveal that topographic and hydrologic factors exert a stronger influence on hotspot formation than urban variables. Among the urban features, population density, road proximity, and waste management infrastructure were more strongly associated with anthropogenic hotspots, while land use exhibited limited influence overall. When multiple parameters were combined, model performance metrics were observed to improve significantly (≥75 % accuracy). The MP algorithm demonstrated more robust generalization across test datasets, whereas XGBoost exhibited signs of overfitting. These findings underscore the value of spatially explicit machine learning models for guiding targeted interventions in plastic pollution mitigation. [Display omitted] •Topographic features were the most influential drivers of natural plastic hotspots.•Combining multiple parameters enhanced classification accuracy above 75 %.•Memetic programming yielded more stable generalization than XGBoost.•Urban parameters had a stronger influence on anthropogenic than natural hotspots. Despite growing global initiatives on sustainable plastic management, less than 10 % of plastic waste is effectively recycled, resulting in widespread environmental dispersion and pollution. This study examines the relative influence of topographic, hydrologic, and urban factors on the proliferation of plastic hotspots (macroplastics) in the urbanized Mfoundi subbasin of Yaoundé, Cameroon. To achieve this, we employed Extreme Gradient Boosting (XGBoost) and Memetic Programming (MP) algorithms to classify both anthropogenic and naturally occurring plastic hotspots based on twelve spatially explicit parameters. This was then followed by assessing model performance through five key metrics: accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results reveal that topographic and hydrologic factors exert a stronger influence on hotspot formation than urban variables. Among the urban features, population density, road proximity, and waste management infrastructure were more strongly associated with anthropogenic hotspots, while land use exhibited limited influence overall. When multiple parameters were combined, model performance metrics were observed to improve significantly (≥75 % accuracy). The MP algorithm demonstrated more robust generalization across test datasets, whereas XGBoost exhibited signs of overfitting. These findings underscore the value of spatially explicit machine learning models for guiding targeted interventions in plastic pollution mitigation.
ArticleNumber	127329
Author	Fantong, Wilson Y. Mabrouk, Emad Kantoush, Sameh A. Shiwomeh, Desmond N. Sumi, Tetsuya Saber, Mohamed Quang Nguyen, Binh Abdrabo, Karim I.
Author_xml	– sequence: 1 givenname: Desmond N. orcidid: 0009-0008-6786-2760 surname: Shiwomeh fullname: Shiwomeh, Desmond N. email: shiwomeh.ndre.38e@st.kyoto-u.ac.jp organization: Department of Urban Management, Graduate School of Engineering, Kyoto University, Kyoto, 612-8133, Japan – sequence: 2 givenname: Sameh A. surname: Kantoush fullname: Kantoush, Sameh A. email: kantoush.samehahmed.2n@kyoto-u.ac.jp organization: Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, 611–0011, Japan – sequence: 3 givenname: Mohamed surname: Saber fullname: Saber, Mohamed email: mohamedmd.saber.3u@kyoto-u.ac.jp organization: Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, 611–0011, Japan – sequence: 4 givenname: Tetsuya surname: Sumi fullname: Sumi, Tetsuya email: sumi.tetsuya.2s@kyoto-u.ac.jp organization: Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, 611–0011, Japan – sequence: 5 givenname: Wilson Y. surname: Fantong fullname: Fantong, Wilson Y. email: fantongy@gmail.com organization: Center of Research for Water and Climate Change, Institute of Geological and Mining Research (IRGM), P.O. Box 4110, Yaoundé, Cameroon – sequence: 6 givenname: Binh orcidid: 0000-0001-6292-5349 surname: Quang Nguyen fullname: Quang Nguyen, Binh email: nqbinh@dut.udn.vn organization: The University of Danang - University of Science and Technology, 54 Nguyen Luong Bang, Danang, Viet Nam – sequence: 7 givenname: Karim I. surname: Abdrabo fullname: Abdrabo, Karim I. email: m.karim.ibrahim@cu.edu.eg organization: Faculty of Urban and Regional Planning, Cairo University, Giza, 12613, Egypt – sequence: 8 givenname: Emad orcidid: 0000-0002-8039-0728 surname: Mabrouk fullname: Mabrouk, Emad email: emad.mabrouk@aum.edu.kw organization: College of Engineering and Technology, American University of the Middle East, Kuwait
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Keywords	Memetic programming algorithm Anthropogenic plastic hotspots Natural plastic hotspots Plastic debris dispersal Influence parameters XGBoost model Proliferation of plastic hotspots
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Snippet	Despite growing global initiatives on sustainable plastic management, less than 10 % of plastic waste is effectively recycled, resulting in widespread... Despite growing global initiatives on sustainable plastic management, less than 10% of plastic waste is effectively recycled, resulting in widespread...
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SubjectTerms	Algorithms Anthropogenic plastic hotspots Boosting Machine Learning Algorithms Cameroon Environmental Monitoring - methods Environmental Pollution - statistics & numerical data Geologic Sediments - chemistry Influence parameters Memetic programming algorithm Natural plastic hotspots Plastic debris dispersal Plastics - analysis Proliferation of plastic hotspots XGBoost model
Title	Using XGBoost and memetic programming to identify hotspots of sediment plastic pollution
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