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Open AccessFeature PaperArticle

A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping

1
Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran, 14115-111 Iran
2
Department of Natural Resources and Environment Engineering, College of Agriculture, Shiraz University, Shiraz, 71441-65186 Iran
3
Department of Watershed Management, Faculty in Natural Resources, Tehran University, Tehran, 14174-14418 Iran
4
Department of Geoinformatics – Z_GIS, University of Salzburg, 5020 Salzburg, Austria
*
Author to whom correspondence should be addressed.
Water 2019, 11(10), 2076; https://doi.org/10.3390/w11102076
Received: 8 August 2019 / Revised: 30 September 2019 / Accepted: 2 October 2019 / Published: 5 October 2019
(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)
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.
Keywords: gully erosion; random forest; KNN; geo-environmental factors; Robat Turk area gully erosion; random forest; KNN; geo-environmental factors; Robat Turk area
MDPI and ACS Style

Avand, M.; Janizadeh, S.; Naghibi, S.A.; Pourghasemi, H.R.; Khosrobeigi Bozchaloei, S.; Blaschke, T. A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping. Water 2019, 11, 2076.

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