The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

中文翻译：

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运用四种机器学习和统计模型评估印度河上游盆地的地质灾害敏感性

中巴经济走廊（CPEC）项目穿过巴基斯坦北部的喀喇昆仑公路，巴基斯坦是世界上最危险的地区之一。该地区最常见的危害是滑坡和泥石流，每年造成生命损失和严重的基础设施破坏。这项研究通过考虑四种独立的机器学习和统计方法，即对数回归（LR），香农熵（SE），证据权重（WoE）和频率，评估了地质灾害（滑坡和泥石流）并开发了敏感性图。比率（FR）模型。为此，使用遥感技术结合实地观察和历史灾害数据集编制了地质灾害清单。十三种条件因素的空间关系，即坡度（度），到断层的距离，地质，高程，分析了到河流的距离，坡度，到道路的距离，年平均降雨量，归一化差异植被指数，剖面曲率，水流功率指数，地形湿度指数和土地覆盖，并分析了灾害分布。结果表明，断层，坡度，高程，岩性，土地覆盖率和年平均降雨量在控制研究区地质灾害的空间分布方面起着关键作用。最终敏感性图针对地面真相并通过绘制“接收器工作特性”（AUROC）曲线下的面积进行了验证。根据AUROC曲线，LR，WoE，FR和SE模型的成功率分别为85.30％，76.00、74.60％和71.40％，其预测率分别为83.10％，75.00％，73.50％和70.10％ ， 分别; 这些值显示了LR在其他三个模型上的更高性能。此外，分别有11.19％，9.24％，10.18％，39.14％和30.25％的区域分别对应于极高，高，中，低和极低磁化率的类别。相关政府官员可以使用已开发的地质灾害敏感性地图，以在区域范围内顺利实施CPEC项目。