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Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach

  • Coastal Vulnerability in West and Central Africa
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Abstract

The coast of Cameroon, located at the bottom of the Gulf of Guinea, is confronted with coastal hazards whose magnitude, distribution, and consequences are currently largely underestimated if not poorly understood. This study aims to fill this gap by proposing an integrated approach to coastal vulnerability assessment, combining simple traditional methods, multicriteria AHP (analytic hierarchy process) analysis, and machine learning techniques. Using geospatial data, field observations, and numerical models, we assessed the 402-km Cameroon coastline, taking into account interactions between physical, geological, and socio-economic factors. The results highlight geomorphology, slope, coastal erosion, and population density as the main contributors to vulnerability. The Integrated Coastal Vulnerability Index (IVCI) calculated by the simple method shows variable levels of vulnerability, with a predominance of “very low” and “low” in the northern sectors (S1 = 58%, S2 = 99%, and S3 = 87%) and “high” and “very high” in the south (S4 = 58% and S5 = 61%). The AHP method reveals a more balanced distribution of vulnerability levels, highlighting a sector (S3 = 96%) at “very strong” and “strong” risk. The application of six machine learning algorithms shows good predictive capabilities for ICVI, with the exception of the support vector machine (SVM). The artificial neural network (ANN) algorithm stands out for its superior accuracy, with an F-score of 0.9, ability to explain data variance (R = 0.95), accurate predictions (RMSE = 0.2), and excellent ability to distinguish classes (kappa coefficient of 0.9 and ROC AUC of 0.9). This study emphasizes the magnitude and complexity of interactions as indicators of the susceptibility of coastal populations to vulnerability.

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Data Availability

The data used in this study include both freely available data and data with restricted access. The freely accessible data can be downloaded from the link available in the manuscript. Restricted or sensitive data are available on request, subject to confidentiality conditions and prior agreements with data providers.

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Acknowledgements

We extend our profound gratitude to the various sources and institutions that played a significant role in the completion of this study. We wish to express our sincere thanks to IRGM for providing geomorphological data and to INC for making old maps and photographs available. We also appreciate the ECMWF ERA-Interim dataset (www.ECMWF.Int/research/Era) for the valuable information provided, earthexplorer.usgs.gov, and Google Earth Engine (GEE) for access to Landsat images. Our thanks go to the Copernicus program of the ESA (https://scihub.copernicus.eu/dhus/) for Sentinel images, CNES/LEGOS/CLS/AVISO for the production of the global tide model FES2014 and X-TRACK/ALES altimetric data. We acknowledge the EU Copernicus Marine Service (https://marine.copernicus.eu/access-data, https://doi.org/https://doi.org/10.48670/moi-00016) and NASA (EOSDIS) Socioeconomic Data And Applications Center (SEDAC) for providing socio-economic data. The commitment and contribution of these entities have significantly enriched our research.

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Authors and Affiliations

  1. IRGM Institute of Geological and Mining Research, Vogt Street, BP, 4110, Yaoundé, Cameroon

    Njutapvoui F. Nourdi

  2. University of Douala, University Institute of Technology, BP, 8698, Douala, Cameroon

    Njutapvoui F. Nourdi & Onguene Raphael

  3. Research Laboratory in Geodesy, National Institute of Cartography, Yaoundé, Cameroon

    Yap Loudi

  4. LASTIG, Univ Gustave Eiffel, ENSG, IGN, 77454, Marne-La-Vallée, France

    Jean-Paul Rudant

  5. Institute of Fisheries and Aquatic Sciences, Yabassi, Cameroon

    Tomedi E. Minette

  6. Research Center of Water and Climate Changes, Yaoundé, Cameroon

    Njutapvoui F. Nourdi, Ntonga Jean Claude & Ntchantcho Romaric

  7. African Regional Centre for Space Science and Technology in French Language, UN (CRASTE-LF), Rabat, Morocco

    Njutapvoui F. Nourdi, Mohammed Achab & Pouwédéou Kambia

  8. Department of Geology and Remote Sensing, Mohammed V University, Scientific Institute Avenue Ibn Battota, B.P. 703, 10090, Agdal Rabat, Morocco

    Mohammed Achab

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Nourdi, N.F., Raphael, O., Achab, M. et al. Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach. Estuaries and Coasts (2024). https://doi.org/10.1007/s12237-024-01362-7

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