A hybrid approach of rough-fuzzy inference system for land degradation susceptibility mapping (case study: Khanmirza agricultural plain-Iran)

Background Land degradation is considered a serious social, economic, and environmental issue in all parts of the world. The fight against this deleterious phenomenon is now an international priority. The mapping of land degradation entails the implementation of an analytical geospatial model to appraise and categorize the severity of land degradation across a region. This paper proposes a rough-fuzzy inference system (RFIS) for detect the most susceptibility area to land degradation. Rough set algorithm was employed to extract IF-THEN rules and fuzzy inference systems were applied in land degradation susceptibility mapping. We utilized this integrated approach to facilitate modeling of the susceptibility of areas to degradation in the Khanmirza agricultural plain in the southwest of Iran. Results The findings of the integrated approach revealed that 11.75% of the region faces high and very high susceptibility to land degradation, which is the major distribution in the central and marginal parts of the study area. The results of the present study indicates that those places have the lowest quality soil (erosion and ECs), and the lowlands are seriously menaced by degradation. Conclusions The evidence taken from field surveys confirmed the efficiency of the integrated approach for land degradation susceptibility mapping. The proposed approach can also produce more reasonable and understandable rules and superlative results in modeling the land degradation susceptibility.