A Remote Sensing Based Approach for the Assessment of Debris Flow Hazards Using Artificial Neural Network and Binary Logistic Regression Modeling

Efforts to map the distribution of debris flows, to assess the factors controlling their development, and to identify the areas prone to their development are often hampered by the absence or paucity of appropriate monitoring systems and historical databases and the inaccessibility of these areas in many parts of the world. We developed methodologies that heavily rely on readily available observations extracted from remote sensing datasets and successfully applied these techniques over the the Jazan province, in the Red Sea hills of Saudi Arabia. We first identified debris flows (10,334 locations) from high spatial resolution satellite datasets (e.g., GeoEye, Orbview), and verified a subset of these occurrences in the field. We then constructed a GIS to host the identified debris flow locations together with co-registered relevant data (e.g., lithology, elevation) and derived products (e.g., slope, normalized difference vegetation index, etc). Spatial analysis of the data sets in the GIS sets indicated various degrees of correspondence between the distribution of debris flows and various variables (e.g., stream power index, topographic position index, normalized difference vegetation index, distance to stream, flow accumulation, slope and soil weathering index, aspect, elevation) suggesting a causal effect. For example, debris flows were found in areas of high slope, low distance to low stream orders and low vegetation index. To evaluate the extent to which these factors control landslide distribution, we constructed and applied: (1) a stepwise input selection by testing all input combinations to make the final model more compact and effective, (2) a statistic-based binary logistic regression (BLR) model, and (3) a mathematical-based artificial neural network (ANN) model. Only 80% (8267 locations) of the data was used for the construction of each of the models and the remaining samples (2067 locations) were used for the accuracy assessment purposes. Results indicate: (1) 95.8% accuracy for the ANN model and 96.4% for the BLR model, (2) more than 97% of the predictions from each of the two models were common between the two models, and (3) the optimum factors for debris flow prediction are: distance to stream, slope, topographic position index 100 (i.e. curvature) and normalized difference vegetation index. Our findings indicate that the adopted methodologies are reliable and cost-effective and could potentially be applied over many of the world's inaccessible arid and semi-arid mountainous lands.