Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND

Abstract

The main objective of this study is to assess the relative contribution of the state-of-the-art topo-hydrological factor, known as height above the nearest drainage (HAND), to landslide susceptibility modellling using three novel statistical models: weights-of-evidence (WofE), index of entropy and certainty factor. In total, 12 landslide conditioning factors that affect the landslide incidence were used as input to the models in the Ziarat Watershed, Golestan Province, Iran. Landslide inventory was randomly divided into a ratio of 70:30 for training and validating the results of the models. The optimum combination of conditioning factors was identified using the principal components analysis (PCA) method. The results demonstrated that HAND is the defining factor among hydrological and topographical factors in the study area. Additionally, the WofE model had the highest prediction capability (AUPRC = 74.31%). Therefore, HAND was found to be a promising factor for landslide susceptibility mapping.     

Application of the coupled TOPSIS–Mahalanobis distance for multi-hazard-based management of the target districts of the Golestan Province,Iran

Natural Hazards - This study is aimed at producing an improved ranking method by coupling the technique for the order of preference by similarity to ideal solution (TOPSIS) and Mahalanobis distance...     

Landslide susceptibility assessment in the Anfu County,China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND)

The present study is aimed at producing landslide susceptibility map of a landslide-prone area (Anfu County, China) by using evidential belief function (EBF), frequency ratio (FR) and Mahalanobis distance (MD) models. To this aim, 302 landslides were mapped based on earlier reports and aerial photographs, as well as, carrying out several field surveys. The landslide inventory was randomly split into a training dataset (70%; 212landslides) for training the models and the remaining (30%; 90 landslides) was cast off for validation purpose. A total of sixteen geo-environmental conditioning factors were considered as inputs to the models: slope degree, slope aspect, plan curvature, profile curvature, the new topo-hydrological factor termed height above the nearest drainage (HAND), average annual rainfall, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), soil texture, and land use/cover. The validation of susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC). As a results, the FR outperformed other models with an AUROC of 84.98%, followed by EBF (78.63%) and MD (78.50%) models. The percentage of susceptibility classes for each model revealed that MD model managed to build a compendious map focused at highly susceptible areas (high and very high classes) with an overall area of approximately 17%, followed by FR (22.76%) and EBF (31%). The premier model (FR) attested that the five factors mostly influenced the landslide occurrence in the area: NDVI, soil texture, slope degree, altitude, and HAND. Interestingly, HAND could manifest clearer pattern with regard to landslide occurrence compared to other topo-hydrological factors such as SPI, STI, and distance to rivers. Lastly, it can be conceived that the susceptibility of the area to landsliding is more subjected to a complex environmental set of factors rather than anthropological ones (residential areas and distance to roads). This upshot can make a platform for further pragmatic measures regarding hazard-planning actions.     

冬季乌鲁木齐城区和机场雾的特征及地面气象条件对比分析

基于乌鲁木齐城区气象站和城北乌鲁木齐地窝堡国际机场（简称机场）2016—2021年冬季逐小时地面气象观测资料,对近6 a冬季两地雾的特征及其对应的地面气象条件进行对比分析。结果表明,城区和机场3种情景雾日出现的概率较为接近,但是不同级别雾日的出现概率有一定的差别,其中大雾日数以两地同时出现为主,概率最高达到19.6%。两地不同强度雾日数和雾过程的频次均呈明显下降趋势,其中机场大雾日数和过程频次均略多于城区。城区雾起始时间有33.3%集中在1—4时,而机场25.2%出现在8—10时。城区雾强度总体强于机场雾,机场雾日最小能见度均值(412.9 m)高于城区(360.8 m)。此外,在冬季准噶尔盆地的“冷湖效应”和山谷风的共同作用下,机场一带盛行的西北偏北和偏北风导致温度相对较低,有利于降温形成大雾天气,城区和机场分别在-12～-4 ℃和-16～-8 ℃的温度区间内出现雾的频率最高,比例分别高达57.4%和50.1%。