Badland erosion susceptibility mapping using machine learning data mining techniques, Firozkuh watershed, Iran

Abstract

Badlands are landforms related to runoff, with dissected V-shaped valleys, short steep slopes, and high drainage density, and results from a very important type of erosion that develops due to a complex interaction of conditioning factors, including climatic, hydrologic, geologic and soil properties, topographic characteristics, and land use. The main goals of this study were (1) create badland susceptibility maps of the Firozkuh watershed and five machine learning algorithms (models)—functional discriminant analysis (FDA), generalized linear model (GLM), mixture discriminant analysis (MDA), multivariate adaptive regression spline (MARS), and support vector machine (SVM); and (2) compare the accuracy of these models. Sixteen conditioning factors were chosen to model and classify badland susceptibility based on a literature review, data availability, and field surveys. Model accuracy was assessed using ROC curve and AUC analyses. The analyses showed that SVM was “excellent,” MARS was “very good,” MDA was “good,” and GLM and FDA were “moderate” in classification accuracy. The land area of the very high and high classes ranged from 31 to 51% of the Firozkuh watershed for the GLM and SVM models, respectively. This indicates that badland erosion is a very important problem in the study area. Climatic, hydrologic, geologic, topographic, and soil conditions as well as land use changes render the Firozkuh watershed prone to badland formation and soil erosion which results in substantial socioeconomic losses. Badland susceptibility mapping is an important tool that can be used to improve managing future badland erosion in the Firozkuh watershed and other areas affected by badland erosion.