Spatial prediction of soil erosion susceptibility: an evaluation of the maximum entropy model

• Published in: Earth science informatics Vol. 11; no. 3; pp. 389 - 401
• Main Authors: Pournader, Maryam, Ahmadi, Hasan, Feiznia, Sadat, Karimi, Haji, Peirovan, Hamid Reza
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2018; Springer Nature B.V
• Subjects: Curvature; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Entropy; Erosion; Hydrology; Information Systems Applications (incl.Internet); Land use; Land use management; Lithology; Maximum entropy; Modelling; Moisture content; Ontology; Research Article; Rill erosion; Sediment yield; Simulation and Modeling; Soil conservation; Soil degradation; Soil erosion; Soil investigations; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Spatial analysis; Streams; Training; Wetness index; GIS; Soil erosion; Maximum Entropy; Iran
• ISSN: 1865-0473; 1865-0481
• DOI: 10.1007/s12145-018-0338-6

Soil erosion is considered as the most widespread form of soil degradation which causes serious environmental problems. This study investigates the performance of the maximum entropy (ME) in mapping rill erosion susceptibility in the Golgol watershed, Ilam province, Iran. To this end, ten rill erosion conditioning factors were selected to be employed in the modelling process based on an investigation of the literature. These layers are: elevation, slope percent, aspect, stream power index, topographic wetness index, distance from streams, plan curvature, lithology, land use, and soil. Then, a training dataset of rill erosion locations was used for modelling this phenomenon. The area under receiver operating characteristics curve was used for evaluating the performance of the ME model. In addition, Modified Pacific South-West Inter Agency Committee (MPSIAC) framework was applied and sediment yield was determined for different hydrological units in the study area. At last, Jackknife test was implemented to show the contribution of the factors in the modelling process. The results depicted that area under ROC curve for training and validation datasets were 0.867, and 0.794, respectively. Therefore, this conclusion can be achieved that ME worked well and could be a good tool for generating rill erosion susceptibility maps and its output could be employed for soil conservation in similar areas.