This study aimed to map the landslide susceptibility in the Chemoga watershed, Ethiopia, using Geographic Information System (GIS) and bivariate statistical models. Based on Google earth imagery and field survey, about 169 landslide locations were identified and classified randomly into training datasets (70%) and test datasets (30%). Eleven landslides conditioning factors, including slope, elevation, aspect, curvature, topographic wetness index, normalized difference vegetation index, road, river, land use, rainfall, and lithology were integrated with training landslides to determine the weights of each factor and factor classes using both frequency ratio (FR) and information value (IV) models. The final landslide susceptibility map was classified into five classes: very low, low, moderate, high, and very high. The results of area under the curve (AUC) accuracy models showed that the success rates of the FR and IV models were 87.00% and 90.10%, while the prediction rates were 88.00% and 92.30%, respectively. This type of study will be very useful to the local government for future planning and decision on landslide mitigation plans.

中文翻译：

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使用 GIS 和双变量统计模型绘制埃塞俄比亚 Chemoga 流域的滑坡敏感性图

本研究旨在利用地理信息系统 (GIS) 和双变量统计模型绘制埃塞俄比亚 Chemoga 流域滑坡敏感性图。基于谷歌地球图像和实地调查，识别出约 169 个滑坡位置，并随机分为训练数据集（70%）和测试数据集（30%）。将坡度、高程、坡向、曲率、地形湿度指数、归一化植被指数、道路、河流、土地利用、降雨和岩性等 11 个滑坡调节因子与训练滑坡相结合，确定每个因子和因子类别的权重频率比（FR）和信息值（IV）模型。最终的滑坡敏感性图分为五个等级：极低、低、中、高和极高。曲线下面积（AUC）准确率模型结果显示，FR和IV模型的成功率分别为87.00%和90.10%，预测率分别为88.00%和92.30%。此类研究对于当地政府未来滑坡缓解计划的规划和决策非常有用。