Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiplecriteria decisionmaking (MCDM), statistical, and machine learning (ML)based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC  0.947, AUPRC  0.948, accuracy  0.849 and kappa  0.699). The second best is the random forest (RF) model (AUSRC  0.965, AUPRC  0.932, accuracy  0.812 and kappa  0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC  0.871, AUPRC  0.867, accuracy  0.758 and kappa  0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)based ML models can successfully map gully erosion susceptibility, particularly in lowincome and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.

Model prediction of depthspecific soil texture distributions with artificial neural network: A case study in Yunfu, a typical area of Udults Zone, South China Abstract   The depthspecific soil texture map with highresolution (i.e. xe2x89xa410xc2xa0m) is essential for soil management and forest silviculture. The objective of this research was to develop a modelling method to generate highresolution soil texture maps at five depths (D1: 0xe2x80x9320, D2: 20xe2x80x9340, D3: 40xe2x80x9360, D4: 60xe2x80x9380, and D5: 80xe2x80x93100xc2xa0cm) in Yunfu, a typical area of Udults Zone, South China. Taking a coarseresolution soil texture (CST) map with a 1: 2,800,000 scale and nine topohydrologic variables derived from a digital elevation model (DEM) with 10xc2xa0mresolution as input candidates, a series of artificial neural network (ANN) models for five depths were built and evaluated by a 10fold crossvalidation with 385 soil profiles from the Yunfu forest. The results indicated that the optimal model for five depths engaged five, five, five, four, and four DEMgenerated variables as inputs, respectively, and model accuracies for estimating sand and clay contents varied with root mean squared error (RMSE) of 6.8xe2x80x939.7%, R2 of 0.56xe2x80x930.72, and relative overall accuracy (ROA)xc2xa0xc2xb1xc2xa05% of 54xe2x80x9381%, which were better than most of other researches. An extra independent validation with 64 soil profiles outside of the modelbuilding area also indicated that the optimal models had adequate capabilities for generalization with RMSE of 9.2xe2x80x9312.2%, R2 of 0.33xe2x80x930.47, and ROAxc2xa0xc2xb1xc2xa05% of 37xe2x80x9353%. The depthspecific sand and clay content maps with 10xc2xa0mresolution generated from the optimal models in Yunfu showed more detailed information than the CST map, and could reflected the influence of the DEMderived topohydrologic variables. Based on the generated maps, horizontal characteristics of soil texture in the study area exhibited an obvious process of clay translocation from the topsoil (D1) to subsoil (D25), a maximum accumulation of clay in D4, and a dominant sandy soil in the topsoil (D1). Thus, the modelling method, i.e. developing ANNs with kfold crossvalidation, can be used to generate depthspecific soil texture maps in Udults Zone, South China. In addition, the generated highresolution maps can clearly show the changes of soil texture in threedimension.