Gully erosion threatens land sustainability. Gullies trigger considerable erosion, damaging agricultural land, infrastructure and urban areas; thus, predicting and modelling gully susceptibility is of utmost concern. In particular, such a model is urgently required in semiarid areas where soil loss from gullies is high. Three predictive models are evaluated to assess gully erosion susceptibility mapping (GESM) in Semnan Province, Iran. The index of entropy (IOE), frequency ratio (FR) and certainty factor (CF) models are combined with remote sensing and geographic information system techniques to predict gully erosion. The collation of data from geographic resources identified 287 gullies in the study area. These areas were then randomly divided into 2 groups for calibration (70% or 201 gullies) and validation (30% or 86 gullies). Pairwise linear dependency amongst geoenvironmental factors was also assessed. A total of 16 factors were screened for modelling. Four performance metrics, namely, true skill statistic (TSS), area under the receiver operating characteristic (AUROC) curve, seed cell area index (SCAI) and modified SCAI (mSCAI), were used to evaluate the prediction accuracy and robustness of each model using validation datasets. Bootstrapped replicates were considered in estimating the accuracy and robustness of each model by varying gully/no-gully samples. The IOE results indicated that elevation, lithology and slope angle promoted favourable conditions for gully erosion in the study area. The results showed that the IOE model performed better than the FR and CF models for all three validation datasets (AUROC_mean = 0.874 and TSS_mean = 0.855). This finding was also confirmed in terms of stability and robustness (R_TSS = 0.024 and R_AUROC = 0.023). The SCAI and mSCAI results showed that all the models exhibited acceptable accuracy, but IOE demonstrated superior performance. Accordingly, IOE was used as the reference model for the study area, indicating that 19.75% and 9.44% of the study area are included in the predicted high and very high susceptibility classes, respectively. Considering the accuracy of GESM, IOE is a reliable tool for decision-making, management and land use planning within the region.

沟壑侵蚀威胁着土地的可持续性。沟渠引发了严重的侵蚀，破坏了农业用地，基础设施和城市地区；因此，预测和模拟沟壑敏感性最为重要。特别是在沟壑区土壤流失高的半干旱地区，迫切需要这种模型。评估了三种预测模型，以评估伊朗塞姆南省的沟壑侵蚀敏感性地图（GESM）。熵指数（IOE），频率比（FR）和确定性因子（CF）模型与遥感和地理信息系统技术相结合以预测沟壑侵蚀。来自地理资源的数据整理确定了研究区域中的287个沟壑。然后将这些区域随机分为两组，分别进行校准（70％或201口）和验证（30％或86口）。还评估了地质环境因素之间的成对线性相关性。共筛选了16个因素进行建模。四个性能指标，分别是真实技能统计量（TSS），接收者工作特征曲线下的面积（AUROC），种子细胞面积指数（SCAI）和改进的SCAI（mSCAI），用于评估每个模型的预测准确性和鲁棒性使用验证数据集。通过改变沟壑/无沟壑的样本来估计每个模型的准确性和鲁棒性时，应考虑采用自举法进行重复。IOE结果表明，仰角，岩性和倾斜角为研究区的沟壑侵蚀提供了有利条件。结果表明，对于所有三个验证数据集（AUROC），IOE模型的性能均优于FR和CF模型。_平均值 = 0.874，TSS_平均值 = 0.855）。稳定性和鲁棒性也证实了这一发现（R _TSS  = 0.024和R _AUROC = 0.023）。SCAI和mSCAI结果表明，所有模型均显示出可接受的准确性，但IOE表现出优异的性能。因此，将IOE用作研究区域的参考模型，表明研究区域的19.75％和9.44％分别包含在预测的高和非常高的磁化率类别中。考虑到GESM的准确性，IOE是该区域内决策，管理和土地使用规划的可靠工具。