山地城市滑坡灾害空间分布特征及影响因素分析

针对山地城市滑坡灾害影响区域的不确定性,选择重庆市中心城区典型滑坡作为研究对象,利用最邻近指数、空间热点探测与核密度估计方法分析了历史滑坡灾害点的空间分布特征;并选择高程、坡度、坡向、地貌类型、土壤类型、土壤侵蚀、降雨、水系、地表覆盖、归一化植被指数(NDVI)、人口密度和道路等12个影响因素建立滑坡因子数据库,利用神经网络模型分析滑坡灾害空间分布特征的驱动因素,并定量计算各影响因子的贡献权重。利用受试者工作特征曲线(ROC)对模型进行准确性评估。最邻近指数结果表明研究区历史滑坡灾害点呈聚集型分布特征,空间热点探测与核密度估计均显示渝中区、沙坪坝区和巴南区北部是滑坡聚集程度最大的地区;在所有的影响因子中,人口密度、地貌类型和降雨对研究区滑坡灾害的空间分布影响最大,而坡向和道路影响最低。ROC曲线下面积AUC值达到0.917,表明该神经网络模型能准确反映出该地区滑坡影响因子的影响程度。

Spatial distribution characteristics and influencing factors of landslide disasters in mountain cities

In order to investigate the uncertainty of landslide disasters affected areas in mountain cities, the typical landslides in the central urban area of Chongqing were selected as the research objects, and the spatial distribution characteristics of historical landslide disaster points were analyzed by the nearest neighbor index, spatial hotspot detection and kernel density estimation methods. A landslide factor database was established with 12 influencing factors including elevation, slope, aspect, landform type, geological lithology, soil type, soil erosion, rainfall, water system, land use, normalized difference vegetation index (NDVI), and population density. A neural network model was used to analyze driving factors of spatial distribution characteristics of landslide disasters and quantitatively calculate the contribution weight of each influencing factor. The accuracy of the model was evaluated with the receiver operating characteristic (ROC) curve. The results obtained from the nearest neighbor index analysis show that the historical landslide disaster points in the study area are clustered, and the spatial hotspot detection and kernel density estimation indicate that Yuzhong District, Shapingba District, and northern Banan District are the areas where the landslides are most concentrated. Among all the factors, population density, land use and rainfall occupy the highest weight, while the weight of aspect and road are the lowest. The area value under the ROC curve (AUC) is 0.917, indicating that the model can accurately reflect the impact of landslide influencing factors in the area.