A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping
Mohammadtaghi Avand,
Saeid Janizadeh,
Seyed Amir Naghibi,
Hamid Reza Pourghasemi,
Saeid Khosrobeigi Bozchaloei,
Thomas Blaschke
Affiliations
Mohammadtaghi Avand
Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran
Saeid Janizadeh
Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran
Seyed Amir Naghibi
Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran
Hamid Reza Pourghasemi
Department of Natural Resources and Environment Engineering, College of Agriculture, Shiraz University, Shiraz 71441-65186, Iran
Saeid Khosrobeigi Bozchaloei
Department of Watershed Management, Faculty in Natural Resources, Tehran University, Tehran 14174-14418, Iran
Thomas Blaschke
Department of Geoinformatics – Z_GIS, University of Salzburg, 5020 Salzburg, Austria
This research was conducted to determine which areas in the Robat Turk watershed in Iran are sensitive to gully erosion, and to define the relationship between gully erosion and geo-environmental factors by two data mining techniques, namely, Random Forest (RF) and k-Nearest Neighbors (KNN). First, 242 gully locations we determined in field surveys and mapped in ArcGIS software. Then, twelve gully-related conditioning factors were selected. Our results showed that, for both the RF and KNN models, altitude, distance to roads, and distance from the river had the highest influence upon gully erosion sensitivity. We assessed the gully erosion susceptibility maps using the Receiver Operating Characteristic (ROC) curve. Validation results showed that the RF and KNN models had Area Under the Curve (AUC) 87.4 and 80.9%, respectively. As a result, the RF method has better performance compared with the KNN method for mapping gully erosion susceptibility. Rainfall, altitude, and distance from a river were identified as the most important factors affecting gully erosion in this area. The methodology used in this research is transferable to other regions to determine which areas are prone to gully erosion and to explicitly delineate high-risk zones within these areas.
