Assessment of Ground Instabilities’ Causative Factors Using Multivariate Statistical Analysis Methods: Case of the Coastal Region of Northwestern Rif, Morocco

• Published in: Geosciences (Basel) Vol. 12; no. 10; p. 383
• Main Authors: Tribak, Haytam, Gasc-Barbier, Muriel, El Garouani, Abdelkader
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022; MDPI
• Subjects: Analysis; Applied geology; causative factors assessment; Civil Engineering; Coastal zone; Discriminant analysis; Earth science; Earth Sciences; Engineering Sciences; Evaluation; ground instabilities; Hydrology; Instability; Landslides; Landslides & mudslides; Lithology; Mathematical statistics; MCA; Multivariate analysis; multivariate statistic; Multivariate statistical analysis; northwestern Rif; PCA; Principal components analysis; Risques; Rock falls; Rockfall; Sciences of the Universe; Stability; Statistical analysis; Statistical methods; Statistics; Variables; Morocco
• DOI: 10.3390/geosciences12100383
• ISSN: 2076-3263

An assessment of ground instabilities’ causative factors remains a topical subject. Such studies are rare, and evaluation techniques are still under development. The choice of evaluation technique should take into account the materials available and the objective sought. Statistical analysis methods are the most widely used, with multivariate analysis being the most accurate. The present work evaluates the weights of the influences of the different factors of ground instability of the coastal region between Tetouan and Jebha through multiple correspondence analysis (MCA) and principal component analysis (PCA). The application of both methods requires an accurate ground instability inventory with study sites that are well documented through modalities of causative factors and other descriptive data. The performed MCA shows that lithology has a significant influence on the type of existing instability. It also helped classify the instabilities into five distinct classes according to their modalities and specify the factors that differentiate the classes. The PCA shows that lithology is the most influential factor in landslides, contrary to rockfalls, where a variety of factors can be preponderant.