Debris flow susceptibility mapping in a portion of the Andes and Preandes of San Juan, Argentina using frequency ratio and logistic regression models

Abstract

In this study, the frequency ratio and logistic regression models are applied and verified for the analysis of debris flow susceptibility in a portion of the Dry Frontal Andes and Occidental Preandes of San Juan at approximately 30°S latitude, through aninvestigation based on a Geographic Information System (GIS). The site under study covers an area of 2175.9 km2 with a debris flow area of 42.45 km2. For this purpose, thematic layers including debris flow inventory, lithology, elevation, slope, aspect, and solar radiation were used. The debris flow inventory map was prepared by interpreting aerial photographs and satellite images and was supported by field surveys. Lithology was extracted from an existing geological map. Slope, aspect and solar radiation were calculated from a Digital Elevation Model created from SRTM (Shuttle Radar Topographic Mission) and topographical maps. The relationship between the variables and the debris flow inventory was calculated using the frequency ratio and logistic regression models. Both models helped to produce debris flow susceptibility maps that classified susceptibility into five categories: very low, low, moderate, high and very high. Subsequently, each debris flow susceptibility map was compared with known debris flow locations and tested. The frequency ratio model (accuracy is 82.71%) was more accurate than the logistic regression model (accuracy is 75.64%) for predictons of the high and very high categories.

En este trabajo se aplican, mediante el uso de Sistemas de Información Geográfica, dos modelos estadísticos en la evaluación de la susceptibilidad del terreno a la ocurrencia de flujos de detritos, la relación de frecuencias (Fr) y la regresión logística. El área de estudio comprende un sector de Cordillera Frontal y de Precordillera Occidental a los 30°S de latitud media. Se crearon mapas de elevación, pendiente, insolación, orientaciones, estratigrafía y un inventario de flujos de detritos. Este último, a partir de la interpretación y análisis digital de fotografías aéreas e imágenes satelitales. La estratigrafía fue obtenida a partir de cartas geologicas preexistentes. Las pendientes, orientaciones e insolación fueron calculadas, a partir de un modelo digital de elevaciones. Los mapas de susceptibilidad generados han sido reclasificados en cinco categorías: muy baja, baja, moderada, alta y muy alta. Finalmente, estos mapas, fueron validados espacialmente y como resultado se observa que el modelo Fr predice mejor (82,71%) que la regresión logística (75,64%) para las clases alta y muy alta.