Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios

Abstract

Soil erosion is a serious problem affecting numerous countries, especially, gully erosion. In the current research, GIS techniques and MARS (Multivariate Adaptive Regression Splines) algorithm were considered to evaluate gully erosion susceptibility mapping among others. The study was conducted in a specific section of the Gorganroud Watershed in Golestan Province (Northern Iran), covering 2142.64 km2 which is intensely influenced by gully erosion. First, Google Earth images, field surveys, and national reports were used to provide a gully-hedcut evaluation map consisting of 307 gully-hedcut points. Eighteen gully erosion conditioning factors including significant geoenvironmental and morphometric variables were selected as predictors. To model sensitivity of gully erosion, Multivariate Adaptive Regression Splines (MARS) was used while the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), drawing ROC curves, efficiency percent, Yuden index, and kappa were used to evaluate model efficiency. We used two different scenarios of the combination of the number of replications, and sample size, including 90%/10% and 80%/20% with 10 replications, and 70%/30% with five, 10, and 15 replications for preparing gully erosion susceptibility mapping (GESM). Each one involves a various subset of both positive (presence), and negative (absence) cases. Absences were extracted as randomly distributed individual cells. Therefore, the predictive competency of the gully erosion susceptibility model and the robustness of the procedure were evaluated through these datasets. Results did not show considerable variation in the accuracy of the model, with altering the percentage of calibration to validation samples and number of model replications. Given the accuracy, the MARS algorithm performed excellently in predictive performance. The combination of 80%/20% using all statistical measures including SST (0.88), SPF (0.83), E (0.79), Kappa (0.58), Robustness (0.01), and AUC (0.84) had the highest performance compared to the other combinations. Consequently, it was found that the performance of MARS for modelling gully erosion susceptibility is quite consistent while changes in the testing and validation specimens are executed. The intense acceptable prediction capability of the MARS model verifies the reliability of the method employed for use of this model elsewhere and gully erosion studies since they are qualified to quickly generating precise and exact GESMs (gully erosion sensitivity maps) to make decisions and management edaphic and hydrologic features.

Pollution Assessment Based on Element Concentration of Tree Leaves and Topsoil in Ayutthaya Province, Thailand

“…Splitting the database resulted in non-linear connections between the humus and the studied elements; thus, the MARS algorithm having no assumptions on distribution on the input data was more successful with better performance ( Figure 5 ). Better performance of MARS had been proven in other studies too [ 69 , 70 ], and we also found it an effective prediction tool and, in this case, a technique for better exploration of relationships among different variables.…”

CMIP5 climate projections and RUSLE-based soil erosion assessment in the central part of Iran

“…SE is a natural geomorphological process (erosion, transport and sedimentation) but after human disturbances can be considered as a land degradation one, which has been a recurring challenge for decades over the world for stakeholders, and especially, in countries such as Iran. Recently, scientists have examined land degradation indicators including desertification 42 , deforestation 43 , salinization 44 , alkalization of soils 45 , overgrazing 46 , intensive land-use changes 47 , and especially, water and wind erosion 48 , 49 . Many of these studies integrated remote sensing, Geographic Information System (GIS) and the RUSLE approach for the estimation of SE 50 – 55 .…”

The Effect of Hydrology on Soil Erosion

“…Javidan et al, [19] applied GIS (Geographic Information System) techniques and the MARS (Multivariate Adaptive Regression Splines) algorithm to evaluate gully erosion susceptibility mapping in a specific section of the Gorganroud Watershed in Golestan Province (Northern Iran), covering 2142.64 km 2 . They also used Google Earth images and field surveys in combination with national reports to generate a map consisting of 307 gully-headcut points.…”