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

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.