GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 15; p. 2478
• Main Authors: Lei, Xinxiang, Chen, Wei, Avand, Mohammadtaghi, Janizadeh, Saeid, Kariminejad, Narges, Shahabi, Hejar, Costache, Romulus, Shahabi, Himan, Shirzadi, Ataollah, Mosavi, Amir
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2020
• Subjects: Agriculture; Algorithms; Arid regions; Arid zones; Artificial intelligence; Data mining; Datasets; Decision trees; Geomorphology; GIS; Global positioning systems; GPS; Gullies; Gully erosion; head-cut erosion; Hydrology; Iran; Land area; Land use; Learning algorithms; Machine learning; Mapping; Remote sensing; Semi arid areas; Semiarid zones; Soil erosion; Studies; Support vector machines; Susceptibility; susceptibility mapping; Vegetation; Watersheds; Iran; Italy
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12152478

In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility.