Mapping Coastal vulnerability using machine learning algorithms: A case study at North coastline of Sebou estuary, Morocco

Abstract

The coastline of Morocco has severe fragility presented in coastal vulnerability to natural and anthropogenic impacts. Our work aims first to analyze the coastal vulnerability using the CVI index can highlight the potential vulnerabilities associated with different land uses and identify hot spots (accretion/erosion) in the northern area of the estuary of Sebou’, Morocco. The northern area of Sebou estuary experiences high vulnerability since it has a low topography, which in turn leads to different coastal issues (erosion/accretion).The research employs a three-step process to achieve its goal. First, shoreline location was extracted from several satellite images (Landsat TM+ and ETM+) during the last 19 years. Moreover, the rates of shoreline change were extracted by integrating ArcGIS and DSAS. Finally, various machine learning algorithms consisting of support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and artificial neural networks were used to map coastal vulnerability (ANN). A total of 4013 points were used to differentiate between high and low vulnerability classes. Based on data derived from DEM, DSAS, and other sources, the computations took into account 8 predictors of coastal vulnerability (elevation, slope, geomorphology, natural habitat, maximum wave height, sea level rise, tidal range, and shoreline change).The obtained data were divided into 70% for model training and 30% for testing. For the evaluation of the proposed models, several measures were used, namely: precision, recall, F1 score, kappa index, and root mean square error (RMSE). The result showed that RF (precision = 99%, Kappa 0.98), DT (precision = 99%, Kappa index = 0.99), and SVM (precision = 88%, Kappa index = 0.83) outperformed the other methods. The prediction was overlaid on (≈76 km) of the coastline. It was observed that about 20.07% (≈15.25 km) of the coastline is moderately vulnerable, while 10.35% (≈7.86 km) is highly vulnerable and 69.57% (≈52.87 km) has a low vulnerability.The suggested method can actively contribute to the sustainable management of the coastal area and is a useful tool for forecasting coastal vulnerability. At the same time, it aids in planning, designing, and facilitating the implementation of adaption strategies by decision-makers.