Identifying the vulnerability level of an area to soil erosion, particularly gully erosion, is key to the development of an efficient management strategy for policymakers. While efforts into susceptibility mapping of natural disasters have grown in recent years, understanding the most relevant predictive causal factors is still a challenge. As the selection of these factors, among many potentially relevant factors, is an important part of the model selection process, we propose a hybrid intelligent approach for the optimal selection of a set of relevant factors based on logistic regression (LR) and genetic algorithms. In order to verify the effectiveness of the proposed approach, this study also identified areas that were highly susceptible to gully erosion using three different classifiers – namely, the LR, support vector machine (SVM) and k-nearest neighbours (k-NN) techniques. We tested the approach in the Yeli Bedrag watershed in north-eastern Golestan province, Iran. The results showed that the elevation, distance to fault, slope and the index of connectivity were the most important causal factors affecting the successful prediction of gully occurrence. Comparison of maximum True Skill Statistic values showed that increased model sophistication did not necessarily result in a higher level of model performance. In terms of performance, k-NN was superior to the SVM and LR methods. This method can be effectively used for gully erosion susceptibility (GES) zonation in the study area, which is very important to support spatial planning to initiate designing mitigation strategies and conservation needs over a large area, or to plan additional conservation efforts and relocate soil conservation plans. In conclusion, our findings indicate that by incorporating the proposed hybrid intelligent approach, the number of relevant factors for GES mapping was reduced, while classification accuracy was increased.

查明该地区易受土壤侵蚀，特别是沟壑侵蚀的脆弱程度，是制定有效的决策者管理战略的关键。尽管近年来在自然灾害敏感性地图绘制方面的工作有所增加，但了解最相关的预测因果关系仍然是一个挑战。由于在许多潜在相关因素中选择这些因素是模型选择过程的重要组成部分，因此我们提出了一种基于逻辑回归（LR）和遗传算法的最优选择一组相关因素的混合智能方法。为了验证所提方法的有效性，本研究还使用三个不同的分类器（即LR，支持向量机（SVM）和k最近邻（k-NN）技术。我们在伊朗东北Golestan省的Yeli Bedrag流域测试了该方法。结果表明，高程，断层距离，坡度和连通性指数是影响成功预测沟壑发生的最重要因素。最大真实技能统计值的比较表明，模型复杂性的提高不一定会导致更高水平的模型性能。在性能方面，k-NN优于SVM和LR方法。该方法可以有效地用于研究区域的沟壑易感性（GES）分区，这对于支持空间规划，着手设计大面积缓解策略和保护需求非常重要，或计划额外的保护工作并重新安置土壤保护计划。总之，我们的发现表明，通过合并提出的混合智能方法，可以减少GES映射的相关因素，同时可以提高分类精度。