Cobos-Mora Sandra Lucia, Rodriguez-Galiano Victor, Lima Aracely
Centro de Investigación, Innovación y Transferencia de Tecnología (CIITT), Universidad Católica de Cuenca, Cuenca, Ecuador.
Departamento de Geografía Física y Análisis Geográfico Regional, Universidad de Sevilla, Sevilla, Spain.
Heliyon. 2023 Sep 15;9(9):e20170. doi: 10.1016/j.heliyon.2023.e20170. eCollection 2023 Sep.
Landslides are one of the natural phenomena with more negative impacts on landscape, natural resources, and human health worldwide. Andean geomorphology, urbanization, poverty, and inequality make it more vulnerable to landslides. This research focuses on understanding explanatory landslide factors and promoting quantitative susceptibility mapping. Both tasks supply valuable knowledge for the Andean region, focusing on territorial planning and risk management support. This work addresses the following questions using the province of Azuay-Ecuador as a study area: (i) How do EFA and LR assess the significance of landslide occurrence factors? (ii) Which are the most significant landslide occurrence factors for susceptibility analysis in an Andean context? (iii) What is the landslide susceptibility map for the study area? The methodological framework uses quantitative techniques to describe landslide behavior. EFA and LR models are based on a historical inventory of 665 records. Both identified NDVI, NDWI, altitude, fault density, road density, and PC2 as the most significant factors. The latter factor represents the standard deviation, maximum value of precipitation, and rainfall in the wet season (January, February, and March). The EFA model was built from 7 latent factors, which explained 55% of the accumulated variance, with a medium item complexity of 1.5, a RMSR of 0.02, and a TLI of 0.89. This technique also identified TWI, fault distance, plane curvature, and road distance as important factors. LR's model, with AIC of 964.63, residual deviance of 924.63, AUC of 0.92, accuracy of 0.84, and Kappa of 0.68, also shows statistical significance for slope, roads density, geology, and land cover factors. This research encompasses a time-series analysis of NDVI, NDWI, and precipitation, including vegetation and weather dynamism for landslide occurrence. Finally, this methodological framework replaces traditional qualitative models based on expert knowledge, for quantitative approaches for the study area and the Andean region.
山体滑坡是全球范围内对景观、自然资源和人类健康产生负面影响较大的自然现象之一。安第斯山脉的地貌、城市化、贫困和不平等使其更容易发生山体滑坡。本研究的重点是了解山体滑坡的解释性因素，并推动定量易发性制图。这两项任务为安第斯地区提供了有价值的知识，重点是领土规划和风险管理支持。本研究以厄瓜多尔阿苏艾省为研究区域，探讨以下问题：（i）探索性因子分析（EFA）和逻辑回归（LR）如何评估山体滑坡发生因素的重要性？（ii）在安第斯地区背景下，哪些是用于易发性分析的最重要的山体滑坡发生因素？（iii）研究区域的山体滑坡易发性地图是什么样的？该方法框架使用定量技术来描述山体滑坡行为。EFA和LR模型基于665条记录的历史清单。两者都将归一化植被指数（NDVI）、归一化水体指数（NDWI）、海拔、断层密度、道路密度和主成分2（PC2）确定为最重要的因素。后一个因素代表标准差、降水最大值和雨季（1月、2月和 March）的降雨量。EFA模型由7个潜在因子构建而成，解释了55%的累积方差，项目复杂度适中，为1.5，均方根残差（RMSR）为0.02，塔克-刘易斯指数（TLI）为0.89。该技术还将地形湿度指数（TWI）、断层距离、平面曲率和道路距离确定为重要因素。LR模型的赤池信息准则（AIC）为964.63，残差偏差为924.63，曲线下面积（AUC）为0.92，准确率为0.84，卡帕系数（Kappa）为0.68，对坡度、道路密度、地质和土地覆盖因素也显示出统计学意义。本研究包括对NDVI、NDWI和降水的时间序列分析，包括植被和天气动态对山体滑坡发生的影响。最后，该方法框架取代了基于专家知识的传统定性模型，采用了针对研究区域和安第斯地区的定量方法。
