Geospatial assessment of landslide-prone areas in the southern part of Anambra State, Nigeria using classical statistical models

As global greenhouse gas concentrations intensify climate change impacts, the risk of landslides increases, particularly in Southern Anambra State, Nigeria. This ongoing threat endangers lives, farmlands, and property, emphasizing the need to pinpoint susceptible areas for effective prevention and mitigation strategies. Employing four classical statistical models—frequency ratio (FR), Shannon's entropy (SE), the weight of evidence (WoE), and logistic regression (LR)—this study identified classes within conditioning factors contributing to landslide formation. The research also evaluated and contrasted the accuracy of these models, considering their combined application, which remained unexplored. Using high-resolution spatial data, twelve conditioning factors and landslide inventory datasets, divided into training (80%) and testing (20%), susceptibility maps, accuracy, and errors were generated for all the statistical models. All models exhibited good accuracy, with slightly increased error margins within an acceptable range. Susceptibility maps generated highlighted the central region as highly landslide-prone, influenced by geological factors (poorly consolidated formations), slope (> 12.253°), elevation (212 to 328 m), rainfall (516.4 to 585.3 mm), distance to the stream (< 111.7 to 223.4 m), land cover (crops and rangeland), NDVI (< 0.201), and SPI (> 1.827). Comparison of the obtained statistical results revealed similarities and differences in accuracy and model performance; as inconsistencies exist with previous studies, suggesting that although geospatial characteristics influence landslide susceptibility studies, the controlling factors for landslide formation are not universally exclusive. The insights provided by this paper are valuable for decision-makers involved in hazard monitoring and management efforts.