Using XGBoost and memetic programming to identify hotspots of sediment plastic pollution Article Swipe
Desmond N Shiwomeh
,
Sameh A. Kantoush
,
Mohamed Saber
,
Tetsuya Sumi
,
Wilson Y. Fantong
,
Binh Quang Nguyen
,
Karim I. Abdrabo
,
Emad Mabrouk
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.envpol.2025.127329
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.envpol.2025.127329
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.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.envpol.2025.127329
- OA Status
- hybrid
- References
- 61
- OpenAlex ID
- https://openalex.org/W4415681404
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4415681404Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.envpol.2025.127329Digital Object Identifier
- Title
-
Using XGBoost and memetic programming to identify hotspots of sediment plastic pollutionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-10-30Full publication date if available
- Authors
-
Desmond N Shiwomeh, Sameh A. Kantoush, Mohamed Saber, Tetsuya Sumi, Wilson Y. Fantong, Binh Quang Nguyen, Karim I. Abdrabo, Emad MabroukList of authors in order
- Landing page
-
https://doi.org/10.1016/j.envpol.2025.127329Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.envpol.2025.127329Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
61Number of works referenced by this work
Full payload
| id | https://openalex.org/W4415681404 |
|---|---|
| doi | https://doi.org/10.1016/j.envpol.2025.127329 |
| ids.doi | https://doi.org/10.1016/j.envpol.2025.127329 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/41173280 |
| ids.openalex | https://openalex.org/W4415681404 |
| fwci | |
| mesh[0].qualifier_ui | Q000032 |
| mesh[0].descriptor_ui | D010969 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | analysis |
| mesh[0].descriptor_name | Plastics |
| mesh[1].qualifier_ui | Q000379 |
| mesh[1].descriptor_ui | D004784 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | methods |
| mesh[1].descriptor_name | Environmental Monitoring |
| mesh[2].qualifier_ui | Q000737 |
| mesh[2].descriptor_ui | D019015 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | chemistry |
| mesh[2].descriptor_name | Geologic Sediments |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D002163 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Cameroon |
| mesh[4].qualifier_ui | Q000706 |
| mesh[4].descriptor_ui | D004787 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | statistics & numerical data |
| mesh[4].descriptor_name | Environmental Pollution |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D000465 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Algorithms |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D000098404 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Boosting Machine Learning Algorithms |
| type | article |
| title | Using XGBoost and memetic programming to identify hotspots of sediment plastic pollution |
| biblio.issue | |
| biblio.volume | 387 |
| biblio.last_page | 127329 |
| biblio.first_page | 127329 |
| is_xpac | False |
| apc_list.value | 3770 |
| apc_list.currency | USD |
| apc_list.value_usd | 3770 |
| apc_paid.value | 3770 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 3770 |
| language | en |
| locations[0].id | doi:10.1016/j.envpol.2025.127329 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S201530359 |
| locations[0].source.issn | 0269-7491, 1873-6424 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0269-7491 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Environmental Pollution |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Environmental Pollution |
| locations[0].landing_page_url | https://doi.org/10.1016/j.envpol.2025.127329 |
| locations[1].id | pmid:41173280 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Environmental pollution (Barking, Essex : 1987) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/41173280 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5114502898 |
| authorships[0].author.orcid | https://orcid.org/0009-0008-6786-2760 |
| authorships[0].author.display_name | Desmond N Shiwomeh |
| authorships[0].countries | JP |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I22299242 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Urban Management, Graduate School of Engineering, Kyoto University, Kyoto 612-8133, Japan; Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto 611-0011, Japan. Electronic address: [email protected]. |
| authorships[0].institutions[0].id | https://openalex.org/I22299242 |
| authorships[0].institutions[0].ror | https://ror.org/02kpeqv85 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I22299242 |
| authorships[0].institutions[0].country_code | JP |
| authorships[0].institutions[0].display_name | Kyoto University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Desmond N. SHIWOMEH |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Urban Management, Graduate School of Engineering, Kyoto University, Kyoto 612-8133, Japan; Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto 611-0011, Japan. Electronic address: [email protected]. |
| authorships[1].author.id | https://openalex.org/A5021426801 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0919-5097 |
| authorships[1].author.display_name | Sameh A. Kantoush |
| authorships[1].countries | JP |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I22299242 |
| authorships[1].affiliations[0].raw_affiliation_string | Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto 611-0011, Japan. Electronic address: [email protected]. |
| authorships[1].institutions[0].id | https://openalex.org/I22299242 |
| authorships[1].institutions[0].ror | https://ror.org/02kpeqv85 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I22299242 |
| authorships[1].institutions[0].country_code | JP |
| authorships[1].institutions[0].display_name | Kyoto University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sameh A. KANTOUSH |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto 611-0011, Japan. Electronic address: [email protected]. |
| authorships[2].author.id | https://openalex.org/A5077448709 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2420-8132 |
| authorships[2].author.display_name | Mohamed Saber |
| authorships[2].countries | JP |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I22299242 |
| authorships[2].affiliations[0].raw_affiliation_string | Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto 611-0011, Japan. Electronic address: [email protected]. |
| authorships[2].institutions[0].id | https://openalex.org/I22299242 |
| authorships[2].institutions[0].ror | https://ror.org/02kpeqv85 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I22299242 |
| authorships[2].institutions[0].country_code | JP |
| authorships[2].institutions[0].display_name | Kyoto University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mohamed Saber |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto 611-0011, Japan. Electronic address: [email protected]. |
| authorships[3].author.id | https://openalex.org/A5101514645 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1423-7477 |
| authorships[3].author.display_name | Tetsuya Sumi |
| authorships[3].countries | JP |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I22299242 |
| authorships[3].affiliations[0].raw_affiliation_string | Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto 611-0011, Japan. Electronic address: [email protected]. |
| authorships[3].institutions[0].id | https://openalex.org/I22299242 |
| authorships[3].institutions[0].ror | https://ror.org/02kpeqv85 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I22299242 |
| authorships[3].institutions[0].country_code | JP |
| authorships[3].institutions[0].display_name | Kyoto University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Tetsuya Sumi |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto 611-0011, Japan. Electronic address: [email protected]. |
| authorships[4].author.id | https://openalex.org/A5031615999 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Wilson Y. Fantong |
| authorships[4].countries | CM |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210123911 |
| authorships[4].affiliations[0].raw_affiliation_string | Center of Research for Water and Climate Change, Institute of Geological and Mining Research (IRGM), P.O. Box 4110, Yaoundé, Cameroon. Electronic address: [email protected]. |
| authorships[4].institutions[0].id | https://openalex.org/I4210123911 |
| authorships[4].institutions[0].ror | https://ror.org/02hz8mm45 |
| authorships[4].institutions[0].type | facility |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210123911 |
| authorships[4].institutions[0].country_code | CM |
| authorships[4].institutions[0].display_name | Institut de Recherches Géologiques et Minières |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Wilson Y. FANTONG |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Center of Research for Water and Climate Change, Institute of Geological and Mining Research (IRGM), P.O. Box 4110, Yaoundé, Cameroon. Electronic address: [email protected]. |
| authorships[5].author.id | https://openalex.org/A5063880819 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-6292-5349 |
| authorships[5].author.display_name | Binh Quang Nguyen |
| authorships[5].countries | VN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I3129492623 |
| authorships[5].affiliations[0].raw_affiliation_string | The University of Danang - University of Science and Technology, 54 Nguyen Luong Bang, Danang, Vietnam. Electronic address: [email protected]. |
| authorships[5].institutions[0].id | https://openalex.org/I3129492623 |
| authorships[5].institutions[0].ror | https://ror.org/03ecpp171 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I3129492623 |
| authorships[5].institutions[0].country_code | VN |
| authorships[5].institutions[0].display_name | University of Da Nang |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Binh Quang Nguyen |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | The University of Danang - University of Science and Technology, 54 Nguyen Luong Bang, Danang, Vietnam. Electronic address: [email protected]. |
| authorships[6].author.id | https://openalex.org/A5053299285 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-8696-1616 |
| authorships[6].author.display_name | Karim I. Abdrabo |
| authorships[6].countries | EG |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I145487455 |
| authorships[6].affiliations[0].raw_affiliation_string | Faculty of Urban and Regional Planning, Cairo University, Giza 12613, Egypt. Electronic address: [email protected]. |
| authorships[6].institutions[0].id | https://openalex.org/I145487455 |
| authorships[6].institutions[0].ror | https://ror.org/03q21mh05 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I145487455 |
| authorships[6].institutions[0].country_code | EG |
| authorships[6].institutions[0].display_name | Cairo University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Karim I. ABDRABO |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Faculty of Urban and Regional Planning, Cairo University, Giza 12613, Egypt. Electronic address: [email protected]. |
| authorships[7].author.id | https://openalex.org/A5064350210 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-8039-0728 |
| authorships[7].author.display_name | Emad Mabrouk |
| authorships[7].countries | EG, KW |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I2803079837, https://openalex.org/I91041137 |
| authorships[7].affiliations[0].raw_affiliation_string | College of Engineering and Technology, American University of the Middle East, Kuwait; Department of Computer Science, Faculty of Computer & Information, Assiut University, Assiut, Egypt. Electronic address: [email protected]. |
| authorships[7].institutions[0].id | https://openalex.org/I91041137 |
| authorships[7].institutions[0].ror | https://ror.org/01jaj8n65 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I91041137 |
| authorships[7].institutions[0].country_code | EG |
| authorships[7].institutions[0].display_name | Assiut University |
| authorships[7].institutions[1].id | https://openalex.org/I2803079837 |
| authorships[7].institutions[1].ror | https://ror.org/02gqgne03 |
| authorships[7].institutions[1].type | education |
| authorships[7].institutions[1].lineage | https://openalex.org/I2803079837 |
| authorships[7].institutions[1].country_code | KW |
| authorships[7].institutions[1].display_name | American University of the Middle East |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Emad Mabrouk |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | College of Engineering and Technology, American University of the Middle East, Kuwait; Department of Computer Science, Faculty of Computer & Information, Assiut University, Assiut, Egypt. Electronic address: [email protected]. |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.envpol.2025.127329 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-30T00:00:00 |
| display_name | Using XGBoost and memetic programming to identify hotspots of sediment plastic pollution |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-07T23:20:04.922697 |
| primary_topic | |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1016/j.envpol.2025.127329 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S201530359 |
| best_oa_location.source.issn | 0269-7491, 1873-6424 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0269-7491 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Environmental Pollution |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Environmental Pollution |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.envpol.2025.127329 |
| primary_location.id | doi:10.1016/j.envpol.2025.127329 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S201530359 |
| primary_location.source.issn | 0269-7491, 1873-6424 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0269-7491 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Environmental Pollution |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Environmental Pollution |
| primary_location.landing_page_url | https://doi.org/10.1016/j.envpol.2025.127329 |
| publication_date | 2025-10-30 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2992804683, https://openalex.org/W3094878791, https://openalex.org/W4392906955, https://openalex.org/W2589034853, https://openalex.org/W3181608074, https://openalex.org/W4387411724, https://openalex.org/W4390495608, https://openalex.org/W3097488600, https://openalex.org/W4240637767, https://openalex.org/W2071263142, https://openalex.org/W4398288261, https://openalex.org/W4401845391, https://openalex.org/W2801372165, https://openalex.org/W2295598076, https://openalex.org/W4319304207, https://openalex.org/W2133892291, https://openalex.org/W4409500528, https://openalex.org/W4405794610, https://openalex.org/W4281679379, https://openalex.org/W4406423232, https://openalex.org/W2737217596, https://openalex.org/W3006417062, https://openalex.org/W2971884534, https://openalex.org/W3198376161, https://openalex.org/W4387424206, https://openalex.org/W3100126154, https://openalex.org/W4410811901, https://openalex.org/W2037496050, https://openalex.org/W2462717513, https://openalex.org/W4318777257, https://openalex.org/W3031158544, https://openalex.org/W3208757535, https://openalex.org/W2078565791, https://openalex.org/W2038701391, https://openalex.org/W3205359112, https://openalex.org/W2594352094, https://openalex.org/W3043068378, https://openalex.org/W3201041433, https://openalex.org/W3216497412, https://openalex.org/W3163717816, https://openalex.org/W4384830918, https://openalex.org/W4407765600, https://openalex.org/W3118095970, https://openalex.org/W4407804875, https://openalex.org/W4361220716, https://openalex.org/W3109476332, https://openalex.org/W4409288775, https://openalex.org/W4409947956, https://openalex.org/W4404011451, https://openalex.org/W4392878159, https://openalex.org/W4387239017, https://openalex.org/W3017246359, https://openalex.org/W4386697042, https://openalex.org/W3185585276, https://openalex.org/W2911511565, https://openalex.org/W4388602264, https://openalex.org/W4200414440, https://openalex.org/W2396437920, https://openalex.org/W4322502493, https://openalex.org/W4285606255, https://openalex.org/W4396654760 |
| referenced_works_count | 61 |
| abstract_inverted_index.% | 11, 162 |
| abstract_inverted_index.a | 113 |
| abstract_inverted_index.10 | 10 |
| abstract_inverted_index.MP | 165 |
| abstract_inverted_index.To | 52 |
| abstract_inverted_index.by | 85 |
| abstract_inverted_index.in | 19, 44, 195 |
| abstract_inverted_index.is | 15 |
| abstract_inverted_index.of | 12, 31, 40, 49, 178, 185 |
| abstract_inverted_index.on | 4, 37, 76, 116 |
| abstract_inverted_index.to | 66, 158 |
| abstract_inverted_index.we | 55 |
| abstract_inverted_index.The | 164 |
| abstract_inverted_index.and | 23, 34, 61, 70, 100, 109, 130 |
| abstract_inverted_index.for | 191 |
| abstract_inverted_index.key | 91 |
| abstract_inverted_index.the | 28, 38, 45, 123, 183 |
| abstract_inverted_index.use | 143 |
| abstract_inverted_index.was | 82 |
| abstract_inverted_index.(MP) | 64 |
| abstract_inverted_index.This | 25, 81 |
| abstract_inverted_index.When | 148 |
| abstract_inverted_index.both | 68 |
| abstract_inverted_index.five | 90 |
| abstract_inverted_index.land | 142 |
| abstract_inverted_index.less | 8 |
| abstract_inverted_index.more | 135, 168 |
| abstract_inverted_index.road | 128 |
| abstract_inverted_index.test | 172 |
| abstract_inverted_index.than | 9, 119 |
| abstract_inverted_index.that | 107 |
| abstract_inverted_index.then | 83 |
| abstract_inverted_index.were | 134, 151, 156 |
| abstract_inverted_index.with | 138 |
| abstract_inverted_index.Among | 122 |
| abstract_inverted_index.These | 180 |
| abstract_inverted_index.based | 75 |
| abstract_inverted_index.exert | 112 |
| abstract_inverted_index.model | 87, 153 |
| abstract_inverted_index.signs | 177 |
| abstract_inverted_index.study | 26 |
| abstract_inverted_index.this, | 54 |
| abstract_inverted_index.urban | 35, 120, 124 |
| abstract_inverted_index.value | 98, 103, 184 |
| abstract_inverted_index.waste | 14, 131 |
| abstract_inverted_index.while | 141 |
| abstract_inverted_index.(NPV). | 104 |
| abstract_inverted_index.(PPV), | 99 |
| abstract_inverted_index.(≥75 | 161 |
| abstract_inverted_index.across | 171 |
| abstract_inverted_index.global | 2 |
| abstract_inverted_index.models | 190 |
| abstract_inverted_index.reveal | 106 |
| abstract_inverted_index.robust | 169 |
| abstract_inverted_index.twelve | 77 |
| abstract_inverted_index.Despite | 0 |
| abstract_inverted_index.Extreme | 57 |
| abstract_inverted_index.Memetic | 62 |
| abstract_inverted_index.Mfoundi | 47 |
| abstract_inverted_index.Results | 105 |
| abstract_inverted_index.XGBoost | 175 |
| abstract_inverted_index.achieve | 53 |
| abstract_inverted_index.factors | 36, 111 |
| abstract_inverted_index.growing | 1 |
| abstract_inverted_index.guiding | 192 |
| abstract_inverted_index.hotspot | 117 |
| abstract_inverted_index.improve | 159 |
| abstract_inverted_index.limited | 145 |
| abstract_inverted_index.machine | 188 |
| abstract_inverted_index.metrics | 155 |
| abstract_inverted_index.plastic | 6, 13, 41, 73, 196 |
| abstract_inverted_index.through | 89 |
| abstract_inverted_index.whereas | 174 |
| abstract_inverted_index.Boosting | 59 |
| abstract_inverted_index.Gradient | 58 |
| abstract_inverted_index.classify | 67 |
| abstract_inverted_index.density, | 127 |
| abstract_inverted_index.employed | 56 |
| abstract_inverted_index.examines | 27 |
| abstract_inverted_index.explicit | 79, 187 |
| abstract_inverted_index.findings | 181 |
| abstract_inverted_index.followed | 84 |
| abstract_inverted_index.hotspots | 42, 74 |
| abstract_inverted_index.learning | 189 |
| abstract_inverted_index.metrics: | 92 |
| abstract_inverted_index.multiple | 149 |
| abstract_inverted_index.negative | 101 |
| abstract_inverted_index.observed | 157 |
| abstract_inverted_index.overall. | 147 |
| abstract_inverted_index.positive | 96 |
| abstract_inverted_index.relative | 29 |
| abstract_inverted_index.stronger | 114 |
| abstract_inverted_index.strongly | 136 |
| abstract_inverted_index.subbasin | 48 |
| abstract_inverted_index.targeted | 193 |
| abstract_inverted_index.(XGBoost) | 60 |
| abstract_inverted_index.Cameroon. | 51 |
| abstract_inverted_index.Yaoundé, | 50 |
| abstract_inverted_index.accuracy, | 93 |
| abstract_inverted_index.algorithm | 166 |
| abstract_inverted_index.assessing | 86 |
| abstract_inverted_index.combined, | 152 |
| abstract_inverted_index.datasets, | 173 |
| abstract_inverted_index.exhibited | 144, 176 |
| abstract_inverted_index.features, | 125 |
| abstract_inverted_index.formation | 118 |
| abstract_inverted_index.hotspots, | 140 |
| abstract_inverted_index.influence | 30, 115, 146 |
| abstract_inverted_index.naturally | 71 |
| abstract_inverted_index.occurring | 72 |
| abstract_inverted_index.pollution | 197 |
| abstract_inverted_index.recycled, | 17 |
| abstract_inverted_index.resulting | 18 |
| abstract_inverted_index.spatially | 78, 186 |
| abstract_inverted_index.urbanized | 46 |
| abstract_inverted_index.accuracy). | 163 |
| abstract_inverted_index.algorithms | 65 |
| abstract_inverted_index.associated | 137 |
| abstract_inverted_index.dispersion | 22 |
| abstract_inverted_index.hydrologic | 110 |
| abstract_inverted_index.management | 132 |
| abstract_inverted_index.parameters | 150 |
| abstract_inverted_index.pollution. | 24 |
| abstract_inverted_index.population | 126 |
| abstract_inverted_index.predictive | 97, 102 |
| abstract_inverted_index.proximity, | 129 |
| abstract_inverted_index.underscore | 182 |
| abstract_inverted_index.variables. | 121 |
| abstract_inverted_index.widespread | 20 |
| abstract_inverted_index.Programming | 63 |
| abstract_inverted_index.effectively | 16 |
| abstract_inverted_index.hydrologic, | 33 |
| abstract_inverted_index.initiatives | 3 |
| abstract_inverted_index.management, | 7 |
| abstract_inverted_index.mitigation. | 198 |
| abstract_inverted_index.parameters. | 80 |
| abstract_inverted_index.performance | 88, 154 |
| abstract_inverted_index.sustainable | 5 |
| abstract_inverted_index.topographic | 108 |
| abstract_inverted_index.demonstrated | 167 |
| abstract_inverted_index.overfitting. | 179 |
| abstract_inverted_index.sensitivity, | 94 |
| abstract_inverted_index.specificity, | 95 |
| abstract_inverted_index.topographic, | 32 |
| abstract_inverted_index.anthropogenic | 69, 139 |
| abstract_inverted_index.environmental | 21 |
| abstract_inverted_index.interventions | 194 |
| abstract_inverted_index.proliferation | 39 |
| abstract_inverted_index.significantly | 160 |
| abstract_inverted_index.generalization | 170 |
| abstract_inverted_index.infrastructure | 133 |
| abstract_inverted_index.(macroplastics) | 43 |
| cited_by_percentile_year | |
| countries_distinct_count | 5 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |