Abstract

Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the high potential of landslides in the region. The aim of the study was to combine adaptive neuro-fuzzy inference system (ANFIS) with grey wolf optimizer (GWO) and particle swarm optimizer (PSO) algorithms using the outputs of qualitative stepwise weight assessment ratio analysis (SWARA) and quantitative certainty factor (CF) models. To this end, 264 landslide positions and twelve conditioning factors including slope, aspect, altitude, distance to faults, distance to rivers, distance to roads, land use, lithology, rainfall, plan and profile curvature and TWI were then extracted considering regional characteristics, literature review and available data. In the next step, the multi-criteria SWARA decision-making model and CF probability model were used to evaluate a correlation between landslide distribution and conditioning factors. Ultimately, landslide susceptibility maps were generated by ANFIS-GWO and ANFIS-PSO hybrid models and the accuracy of models was assessed by ROC curve. According to the results, the area under the curve (AUC) for the hybrid models \({\text{ANFIS - GWO}}_{{\text{SWARA}}}\), \({\text{ANFIS - PSO}}_{{\text{SWARA}}}\), \({\text{ANFIS - GWO}}_{{\text{CF}}}\) and \({\text{ANFIS - PSO}}_{{\text{CF}}}\) was 0.789, 0.838, 0.850 and 0.879, respectively. The hybrid models \({\text{ANFIS - PSO}}_{{\text{CF}}}\) and \({\text{ANFIS - GWO}}_{{\text{SWARA}}}\) showed the highest and lowest prediction rate, respectively. Moreover, CF outperformed the SWARA method in terms of evaluating correlation between conditioning factors and landslides. The map produced in this study can be used by regional authorities to manage landslide risk.

Graphic abstract

中文翻译：

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新型混合ANFIS模型在滑坡敏感性测绘中的比较性能

摘要

扎格罗斯山脉的卡伦流域发生了许多滑坡。在本研究中，鉴于该地区的滑坡潜力很大，我们采用了一种新颖的比较方法对滑坡进行空间建模。研究的目的是使用定性逐步权重评估比分析（SWARA）和定量确定性因子（SWAF）的输出，将自适应神经模糊推理系统（ANFIS）与灰太狼优化器（GWO）和粒子群优化器（PSO）算法结合起来。 CF）模型。为此，考虑到区域特征，提取了264个滑坡位置和十二个条件因子，包括坡度，坡向，高度，断层距离，到河流的距离，到道路的距离，土地利用，岩性，降雨，平面轮廓曲率和TWI，文献综述和可用数据。在下一步中 利用多准则SWARA决策模型和CF概率模型来评价滑坡分布与条件因素之间的相关性。最终，通过ANFIS-GWO和ANFIS-PSO混合模型生成滑坡敏感性图，并通过ROC曲线评估模型的准确性。根据结果​​，混合模型的曲线下面积（AUC）\（{\ text {ANFIS-GWO}} _ {{\ text {SWARA}}} \\），\（{\ text {ANFIS-PSO}} _ {{\ text {SWARA}}} \），\（ {\ text {ANFIS-GWO}} _ {{\ text {CF}}} \}和\（{\ text {ANFIS-PSO}} _ {{\ text {CF}}} \）为0.789、0.838，分别为0.850和0.879。混合模型\（{\ text {ANFIS-PSO}} _ {{\ text {CF}}} \）和\（{\ text {ANFIS-GWO}} _ {{\ text {SWARA}}} \）分别显示最高和最低的预测率。此外，就评估条件因子和滑坡之间的相关性而言，CF优于SWARA方法。这项研究产生的地图可以被地区当局用来管理滑坡风险。

图形摘要