Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly prone to future landslides is important for decision-making. In the present study, for the first time, the group method of data handling (GMDH) was used to generate landslide susceptibility map for a specific region in Uzbekistan. First, 210 landslide locations were identified by field survey and then divided randomly into model training and model validation datasets (70% and 30%, respectively). Data on nine conditioning factors, i.e., altitude, slope, aspect, topographic wetness index (TWI), length of slope (LS), valley depth, distance from roads, distance from rivers, and geology, were collected. Finally, the maps were validated using the testing dataset and receiver operating characteristic (ROC) curve analysis. The findings showed that the “optimized” GMDH model (i.e., using the gray wolf optimizer [GWO]) performed better than the standalone GMDH model, during both the training and testing phase. The accuracy of the GMDH–GWO model in the training and testing phases was 94% and 90%, compared to 85% and 82%, respectively, for the standard GMDH model. According to the GMDH–GWO model, the study area included very low, low, moderate, high, and very high landslide susceptibility areas, with proportions of 14.89%, 10.57%, 15.00%, 35.12%, and 24.43%, respectively.

中文翻译：

---

基于数据处理模型优化组方法的滑坡敏感性评价

滑坡会造成相当大的生命损失和财产损失，并且是全世界最常见的自然灾害之一。减少破坏的最基本，最简单的方法之一就是准备滑坡灾害图。准确预测高度容易发生未来滑坡的区域对于决策至关重要。在本研究中，首次使用分组数据处理方法（GMDH）生成乌兹别克斯坦特定区域的滑坡敏感性图。首先，通过现场调查确定了210个滑坡位置，然后将其随机分为模型训练和模型验证数据集（分别为70％和30％）。有关九个调节因素的数据，即海拔，坡度，坡向，地形湿度指数（TWI），坡度（LS），谷深，距道路的距离，距河流的距离以及地质，被收集。最后，使用测试数据集和接收器工作特性（ROC）曲线分析对地图进行验证。研究结果表明，在训练和测试阶段，“优化的” GMDH模型（即使用灰太狼优化器[GWO]）的性能要优于独立的GMDH模型。GMDH–GWO模型在训练和测试阶段的准确性分别为94％和90％，而标准GMDH模型的准确性分别为85％和82％。根据GMDH–GWO模型，研究区域包括非常低，低，中，高和非常高的滑坡易感性区域，所占比例分别为14.89％，10.57％，15.00％，35.12％和24.43％。研究结果表明，在训练和测试阶段，“优化的” GMDH模型（即使用灰太狼优化器[GWO]）的性能要优于独立的GMDH模型。GMDH–GWO模型在训练和测试阶段的准确性分别为94％和90％，而标准GMDH模型的准确性分别为85％和82％。根据GMDH–GWO模型，研究区域包括非常低，低，中，高和非常高的滑坡易感性区域，所占比例分别为14.89％，10.57％，15.00％，35.12％和24.43％。研究结果表明，在训练和测试阶段，“优化的” GMDH模型（即使用灰太狼优化器[GWO]）的性能要优于独立的GMDH模型。GMDH–GWO模型在训练和测试阶段的准确性分别为94％和90％，而标准GMDH模型的准确性分别为85％和82％。根据GMDH–GWO模型，研究区域包括非常低，低，中，高和非常高的滑坡易感性区域，所占比例分别为14.89％，10.57％，15.00％，35.12％和24.43％。用于标准GMDH模型。根据GMDH–GWO模型，研究区域包括非常低，低，中，高和非常高的滑坡易感性区域，所占比例分别为14.89％，10.57％，15.00％，35.12％和24.43％。用于标准GMDH模型。根据GMDH–GWO模型，研究区域包括非常低，低，中，高和非常高的滑坡易感性区域，所占比例分别为14.89％，10.57％，15.00％，35.12％和24.43％。