Independent-Target Machine Learning and a 2040 Coastal Governance Horizon for Bonny Island, Niger Delta
Authors
The Institute of Natural Resources, Environment and Sustainable Development (INRES), Nigeria (Nigeria)
The Institute of Natural Resources, Environment and Sustainable Development (INRES), Nigeria (Nigeria)
The Institute of Natural Resources, Environment and Sustainable Development (INRES), Nigeria (Nigeria)
Article Information
DOI: 10.51244/IJRSI.2026.1307000388
Subject Category: Education
Volume/Issue: 13/7 | Page No: 5264-5277
Publication Timeline
Submitted: 2026-08-03
Accepted: 2026-08-08
Published: 2026-08-20
Abstract
Operational coastal-risk decision support in the Niger Delta is constrained by three gaps: the absence of integrated socio-physical risk surfaces, the under-validation of machine-learning models against independent observational targets, and the lack of medium-horizon planning frameworks keyed to specific policy years. This study addresses all three for Bonny Island. A six-parameter Coastal Vulnerability Index was computed, and three classifiers (Random Forest, Gradient Boosting, Multilayer Perceptron) were trained on both the internally-derived CVI risk class and an independent flood-frequency target from the JRC Global Surface Water occurrence band, 1984–2021, with performance reported as Cohen's kappa. Projections under SSP5–8.5 with Niger-Delta subsidence spanned the 2030, 2040, 2050 and 2100 horizons, and exposure integrated WorldPop 2020, Google Open Buildings v3 and OpenStreetMap roads. The CVI surface places 38.3% of Bonny Island (81.80 km²) in the High and Very-High classes. Random Forest reached 0.98 internal accuracy against the CVI target but only 0.564 Cohen's kappa (macro-F1 0.747) against the independent flood-frequency target. This gap, substantial agreement, yet far below the internal score exposes the optimism of self-validated models, while the 0.564 remains competitive with international benchmarks. High-vulnerability corridors hold about 132,374 residents, 17,803 buildings and 282.5 km of road. This gap between internal and independent scores matters because most published vulnerability models report only the former, overstating reliability. For Bonny Island it yields a 2040 Planning Horizon Package across Environment, Life and Property dimensions, aligned with Sendai Framework Targets A–D and directly actionable by Bonny LGA and federal partners. Globally, the open, zero-cost pipeline is transferable to any data-poor coastal region needing defensible, independently validated risk surfaces.
Keywords
Coastal Vulnerability Index; machine learning; Cohen's kappa; socio-economic exposure; WorldPop; Open Buildings; Sendai Framework; Niger Delta; 2040 planning horizon
Downloads
References
1. Adekoya, O.A. and Odufuwa, B.O. (2013) Public perception of tidal flooding hazards on Bonny Island, Rivers State, Nigeria. Marine Science 3: 91-99. [Google Scholar] [Crossref]
2. Adelekan, I.O. (2010) Vulnerability of poor urban coastal communities to flooding in Lagos, Nigeria. Environment and Urbanization 22: 433-450. [Google Scholar] [Crossref]
3. Adger, W.N. (2006) Vulnerability. Global Environmental Change 16: 268-281. [Google Scholar] [Crossref]
4. Akukwe, T.I. and Ogbodo, C. (2015) Spatial analysis of vulnerability to flooding in Port Harcourt metropolis, Nigeria. SAGE Open 5: 1-19. [Google Scholar] [Crossref]
5. Anifowose, B., Lawler, D.M., van der Horst, D. and Chapman, L. (2014) A systematic quality assessment of environmental impact statements in the oil and gas industry. Science of the Total Environment 572: 570-585. [Google Scholar] [Crossref]
6. Bagdanavičiūtė, I., Kelpšaitė, L. and Soomere, T. (2015) Multi-criteria evaluation approach to coastal vulnerability index development in micro-tidal low-lying areas. Ocean and Coastal Management 104: 124-135. [Google Scholar] [Crossref]
7. Belgiu, M. and Drăguţ, L. (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114: 24-31. [Google Scholar] [Crossref]
8. Birkmann, J., Cardona, O.D., Carreño, M.L., Barbat, A.H., Pelling, M., Schneiderbauer, S., … Welle, T. (2013) Framing vulnerability, risk and societal responses: The MOVE framework. Natural Hazards 67: 193-211. [Google Scholar] [Crossref]
9. Boeing, G. (2017) OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems 65: 126-139. [Google Scholar] [Crossref]
10. Breiman, L. (2001) Random forests. Machine Learning 45: 5-32. [Google Scholar] [Crossref]
11. Brown, C.F., Brumby, S.P., Guzder-Williams, B., Birch, T., Hyde, S.B., Mazzariello, J., … Tait, A.M. (2022) Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data 9: 251. [Google Scholar] [Crossref]
12. Brown, S., Nicholls, R.J., Goodwin, P., Haigh, I.D., Lincke, D., Vafeidis, A.T. and Hinkel, J. (2018) Quantifying land and people exposed to sea-level rise with no mitigation and 1.5 °C and 2.0 °C rise in global temperatures to year 2300. Earth's Future 6: 583-600. [Google Scholar] [Crossref]
13. Bui, D.T., Hoang, N.-D., Martínez-Álvarez, F., Ngo, P.-T.T., Hoa, P.V., Pham, T.D., Samui, P. and Costache, R. (2020) A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Science of the Total Environment, 701, 134413. [Google Scholar] [Crossref]
14. Cohen, J. (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 37-46. [Google Scholar] [Crossref]
15. Ericson, J.P., Vörösmarty, C.J., Dingman, S.L., Ward, L.G. and Meybeck, M. (2006) Effective sea-level rise and deltas: Causes of change and human dimension implications. Global and Planetary Change, 50(1-2), 63-82. [Google Scholar] [Crossref]
16. Fox-Kemper, B., Hewitt, H.T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S.S., Edwards, T.L., … Yu, Y. (2021) Ocean, cryosphere and sea level change. In V. Masson-Delmotte et al. (Eds.), Climate change 2021: The physical science basis (pp. 1211-1362). Cambridge University Press. [Google Scholar] [Crossref]
17. Friedman, J.H. (2001) Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29: 1189-1232. [Google Scholar] [Crossref]
18. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202: 18-27. [Google Scholar] [Crossref]
19. Gornitz, V. (1990) Vulnerability of the East Coast, U.S.A. to future sea level rise. Journal of Coastal Research, SI 9, 201-237. [Google Scholar] [Crossref]
20. Hallegatte, S., Green, C., Nicholls, R.J. and Corfee-Morlot, J. (2013) Future flood losses in major coastal cities. Nature Climate Change 3: 802-806. [Google Scholar] [Crossref]
21. Hauer, M.E., Fussell, E., Mueller, V., Burkett, M., Call, M., Abel, K., … Wrathall, D. (2020) Sea-level rise and human migration. Nature Reviews Earth and Environment 1: 28-39. [Google Scholar] [Crossref]
22. Hinkel, J., Lincke, D., Vafeidis, A.T., Perrette, M., Nicholls, R.J., Tol, R.S.J., … Levermann, A. (2014) Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences 111: 3292-3297. [Google Scholar] [Crossref]
23. Intergovernmental Panel on Climate Change. (2021) Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (V. Masson-Delmotte, P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, … B. Zhou, Eds.). Cambridge University Press. [Google Scholar] [Crossref]
24. Intergovernmental Panel on Climate Change. (2022) Climate change 2022: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, … B. Rama, Eds.). Cambridge University Press. [Google Scholar] [Crossref]
25. Iyalomhe, F., Rizzi, J., Pasini, S., Torresan, S., Critto, A. and Marcomini, A. (2015) Regional risk assessment for climate change impacts on coastal aquifers. Science of the Total Environment 537: 100-114. [Google Scholar] [Crossref]
26. James, G.K., Adegoke, J.O., Saba, E., Nwilo, P., Akinyede, J. and Osagie, S. (2013) Social and economic impacts of climate change on the urban environment of Lagos, Nigeria. Current Opinion in Environmental Sustainability 5: 1-9. [Google Scholar] [Crossref]
27. Jones, B. and O'Neill, B.C. (2020) Global one-eighth degree population base year and projection grids based on the Shared Socioeconomic Pathways, Revision 01. NASA Socioeconomic Data and Applications Center (SEDAC). [Google Scholar] [Crossref]
28. Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M. and Brumby, S.P. (2021) Global land use/land cover with Sentinel-2 and deep learning. In IGARSS 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium (pp. 4704-4707). IEEE. [Google Scholar] [Crossref]
29. Landis, J.R. and Koch, G.G. (1977) The measurement of observer agreement for categorical data. Biometrics 33: 159-174. [Google Scholar] [Crossref]
30. Lemos, M.C. and Boyd, E. (2010) The politics of adaptation across scales: The implications of additionality to policy choice and development. In M.R. Redclift and M. Grasso (Eds.), Handbook on climate change and human security (pp. 96-110). Edward Elgar. [Google Scholar] [Crossref]
31. Lwasa, S. (2010) Adapting urban areas in Africa to climate change: The case of Kampala. Current Opinion in Environmental Sustainability 2: 166-171. [Google Scholar] [Crossref]
32. Mahapatra, M., Ratheesh, R. and Rajawat, A.S. (2015) Sea level rise and coastal vulnerability assessment for the Bay of Bengal coast of India. Natural Hazards 76: 139-159. [Google Scholar] [Crossref]
33. Mahmood, R., Ahmed, N., Zhang, L. and Li, G. (2022) Coastal vulnerability assessment of the Niger Delta to sea-level rise using a GIS-based composite index. Estuarine, Coastal and Shelf Science, 270, 107822. [Google Scholar] [Crossref]
34. Maxwell, A.E., Warner, T.A. and Fang, F. (2018) Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing 39: 2784-2817. [Google Scholar] [Crossref]
35. Murali, R.M., Ankita, M., Amrita, S. and Vethamony, P. (2013) Coastal vulnerability assessment of Puducherry coast, India, using the analytical hierarchical process. Natural Hazards and Earth System Sciences 13: 3291-3311. [Google Scholar] [Crossref]
36. Musa, Z.N., Popescu, I. and Mynett, A. (2014) The Niger Delta's vulnerability to river floods due to sea level rise. Natural Hazards and Earth System Sciences 14: 3317-3329. [Google Scholar] [Crossref]
37. O'Neill, B.C., Kriegler, E., Ebi, K.L., Kemp-Benedict, E., Riahi, K., Rothman, D.S., van Ruijven, B.J., van Vuuren, D.P., Birkmann, J., Kok, K., Levy, M. and Solecki, W. (2017) The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change 42: 169-180. [Google Scholar] [Crossref]
38. Pekel, J.-F., Cottam, A., Gorelick, N. and Belward, A.S. (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540: 418-422. [Google Scholar] [Crossref]
39. Pelling, M. (2003) The vulnerability of cities: Natural disasters and social resilience. Earthscan. [Google Scholar] [Crossref]
40. Pendleton, E.A., Thieler, E.R. and Williams, S.J. (2010) Importance of coastal change variables in determining vulnerability to sea- and lake-level change. Journal of Coastal Research 26: 176-183. [Google Scholar] [Crossref]
41. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N. and Prabhat (2019) Deep learning and process understanding for data-driven Earth system science. Nature 566: 195-204. [Google Scholar] [Crossref]
42. Roberts, D.R., Bahn, V., Ciuti, S., Boyce, M.S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J.J., Schroeder, B., Thuiller, W., Warton, D.I., Wintle, B.A., Hartig, F. and Dormann, C.F. (2017) Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40: 913-929. [Google Scholar] [Crossref]
43. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning representations by back-propagating errors. Nature 323: 533-536. [Google Scholar] [Crossref]
44. Saaty, T.L. (1980) The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill. [Google Scholar] [Crossref]
45. Sirko, W., Brempong, E.A., Marcos, J.T.C., Annkah, A., Korme, A.A., Hassen, M.A., … Quinn, J. (2023) High-resolution building and road detection from Sentinel-2 (arXiv:2310.11622). arXiv. [Google Scholar] [Crossref]
46. Sirko, W., Kashubin, S., Ritter, M., Annkah, A., Bouchareb, Y.S.E., Dauphin, Y., … Quinn, J. (2021) Continental-scale building detection from high resolution satellite imagery (arXiv:2107.12283). arXiv. [Google Scholar] [Crossref]
47. Syvitski, J.P.M., Kettner, A.J., Overeem, I., Hutton, E.W.H., Hannon, M.T., Brakenridge, G.R., … Nicholls, R.J. (2009) Sinking deltas due to human activities. Nature Geoscience 2: 681-686. [Google Scholar] [Crossref]
48. Tehrany, M.S., Jones, S. and Shabani, F. (2019) Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. Catena 175: 174-192. [Google Scholar] [Crossref]
49. Thieler, E.R. and Hammar-Klose, E.S. (2000) National assessment of coastal vulnerability to sea-level rise: Preliminary results for the U.S. Atlantic Coast (Open-File Report 99-593). U.S. Geological Survey. [Google Scholar] [Crossref]
50. United Nations Office for Disaster Risk Reduction. (2015) Sendai framework for disaster risk reduction 2015-2030. UNISDR. [Google Scholar] [Crossref]
51. United Nations Office for Disaster Risk Reduction. (2019) Global assessment report on disaster risk reduction 2019. UNDRR, Geneva. [Google Scholar] [Crossref]
52. Vitousek, S., Barnard, P.L., Fletcher, C.H., Frazer, N., Erikson, L. and Storlazzi, C.D. (2017) Doubling of coastal flooding frequency within decades due to sea-level rise. Scientific Reports 7: 1399. [Google Scholar] [Crossref]
53. Wisner, B., Blaikie, P., Cannon, T. and Davis, I. (2004) At risk: Natural hazards, people's vulnerability and disasters (2nd ed.). Routledge. [Google Scholar] [Crossref]
54. WorldPop. (2018) Global high resolution population denominators project, Nigeria (UN-adjusted, 2020). University of Southampton / WorldPop. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
- Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
- “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
- Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
- Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study