Applicability and performance of statistical index, certain factor and frequency ratio models in mapping landslides susceptibility in Rwanda
Lanhai Li, Lamek Nahayo, Gabriel Habiyaremye, Christophe Mupenzi
Abstract
The reduction of landslide losses requires appropriate method to reveal prone areas, causal factors and future occurrence likelihood. This study compared the effectiveness of the Statistical Index (SI), Certain Factor (CF) and Frequency Ratio (FR) models on landslide susceptibility mapping for Rwanda. Historical record and extensive field surveys generated an inventory map of 336 points. Thereafter, 245 and 91 points were randomly selected for building susceptibility and validation, respectively. Ten causal factors: elevation, slope angles, aspects, lithology, soil texture, distance to rivers, distance to roads, rainfall, land use/cover and normalized difference vegetation Index were analyzed. The area under curve (AUC) method revealed the training accuracies of 89.2%, 85.3% and 82.1% for the FR, CF and SI methods, respectively. And the prediction accuracies were 88.7%, 83.9% and 80.2% for the FR, CF and SI methods, respectively. Therefore, the FR performed well in landslide susceptibility mapping over the study area.