共查询到20条相似文献,搜索用时 156 毫秒
1.
在研究广东省崩塌、滑坡、泥石流孕灾环境的基础上,选取高程、坡度、地质年代、岩性、距断层距离、距水系距离、归一化植被指数(NDVI)7个因子作为地质灾害易发条件因子。首先利用CF模型计算出7个因子各分类级别的CF值,然后将各因子的CF值作为自变量,是否发生地质灾害作为因变量,利用Logistic回归模型得到各因子的回归系数。再对各因子之间的独立性进行检验,所选7个因子都符合独立性检验条件,全部进入到逻辑回归方程中,计算出各独立单元发生崩滑流地质灾害的概率。根据计算结果将广东省崩滑流地质灾害易发程度划分成四类:极低易发区(16.63%),低易发区(28.65%),中易发区(32.57%),高易发区(22.15%)。评价模型的合理性和精确度都符合检验要求,说明采用确定性系模型和逻辑回归模型能够较为客观准确地评价广东省地质灾害易发性。 相似文献
2.
3.
兰州是中国省会城市中地质灾害较为频发的地区之一,其地质灾害主要受地质、地形和人类活动等因素的影响。本文在分析兰州市区滑坡发育的控制因素和主要影响因素的基础上,以地理信息系统(GIS)为平台、采用专业数理统计软件SPSS中的逻辑回归模型(Binary Logistic)计算评价区内各单元格地质灾害的发生概率,通过GIS平台和SPSS软件之间数据格式的交换,利用GIS软件的空间分析功能,对兰州市滑坡灾害危险性进行了区划。研究结果与滑坡分布规律一致,对兰州市的地质灾害防治具有实际意义。 相似文献
4.
5.
近年广安区洪水灾害频发诱发大量地质灾害,严重威胁人民的生命财产安全。文章结合广安区地质灾害调查与区划项目,在地质灾害野外实际调查的基础上,选取坡度、坡向、地层岩性、地质构造、水系、降雨作为影响地质灾害发生的评价因子。运用层次分析法确定各评价因子的权重并建立地质灾害危险性指数模型,通过GIS系统的空间分析功能进行栅格运算,完成广安区地质灾害危险性区划。评价结果与实际调查情况基本相吻合,可为今后广安区地质灾害的预测及预防提供了科学参考。 相似文献
6.
以新平县为研究区,采用确定性系数(CF)方法分析滑坡与地质环境因子各区段(类型)之间的敏感性关系,得出各因子不同区段(类型)的敏感性大小,并运用Logistic回归分析方法建立预警区划模型,认为坡度、岩组、年均降雨量、高程、构造等五个因子是影响研究区滑坡发生的敏感性因子,建立Logistic概率预测模型,并对研究区进行五级预警区划,区划结果对该区预警工作有积极的指导意义。 相似文献
7.
8.
在北京市大清河流域生态涵养区1450 km2的区域内,以遥感影像解译为基础,结合1∶50 000地质灾害详细调查,获取全区888个地质灾害隐患点作为样本数据库,选取基岩类型、地貌类型、地形坡度、河流、公路、断裂6个评价因子,采用确定性系数(CF)与Logistic回归耦合模型评价地质灾害易发性,依照自然间断点分级法(Jenks)将研究区划分为极高易发区、高易发区、中易发区、低易发区和极低易发区。将未参与模型训练的20%地质灾害隐患点作为检验点与易发性分区结果进行叠加分析,通过频率比和ROC曲线进行精度检验。结果显示:基岩类型对地质灾害的发育具有控制作用;公路、断裂对地质灾害的空间分布影响明显;CF与Logistic回归耦合模型在实际应用中具有较高的准确性,是一种地质灾害易发性评价可靠性高的模型。 相似文献
9.
G219阿克赛钦段沿线地形地貌多样,地质条件复杂,道路运行严重受崩滑流等地质灾害影响。结合现场调查和遥感解译生成的灾害数据库,分析阿克赛钦段沿线崩滑流灾害分布特征。利用频率比法选取坡度、坡向、高程、工程地质岩组、PGA、距断层距离、距河流距离、距公路距离等8个影响因子,进行崩滑流灾害易发性评价。研究发现:(1)阿克赛钦段崩滑流灾害主要分布在赛图拉-大红柳滩一带,泥石流灾害主要发育在康西瓦断裂附近河谷区域,崩滑灾害基本分布在切坡等扰动活动强烈的康西瓦-大红柳滩一带;(2)研究区地质灾害极高、高易发区主要分布在泉水沟以北河谷及六三五道班南部部分区域,面积分别占研究区的7.99%、21.08%,各有63.71%、23.56%的总历史灾害面积占比;(3)基于频率比法的易发性评价模型AUC值为0.854,评价结果可靠。研究方法及结果可为G219阿克赛钦段防灾减灾工作提供理论依据与指导。 相似文献
10.
11.
Landslide zonation studies emphasize on preparation of landslide hazard zonation maps considering major instability factors contributing to occurrence of landslides. This paper deals with geographic information system-based landslide hazard zonation in mid Himalayas of Himachal Pradesh from Mandi to Kullu by considering nine relevant instability factors to develop the hazard zonation map. Analytical hierarchy process was applied to assign relative weightages over all ranges of instability factors of the slopes in study area. To generate landslide hazard zonation map, layers in geographic information system were created corresponding to each instability factor. An inventory of existing major landslides in the study area was prepared and combined with the landslide hazard zonation map for validation purpose. The validation of the model was made using area under curve technique and reveals good agreement between the produced hazard map and previous landslide inventory with prediction accuracy of 79.08%. The landslide hazard zonation map was classified by natural break classifier into very low hazard, low hazard, moderate hazard, high hazard and very high landslide hazard classes in geographic information system depending upon the frequency of occurrence of landslides in each class. The resultant hazard zonation map shows that 14.30% of the area lies in very high hazard zone followed by 15.97% in high hazard zone. The proposed model provides the best-fit classification using hierarchical approach for the causative factors of landslides having complex structure. The developed hazard zonation map is useful for landslide preparedness, land-use planning, and social-economic and sustainable development of the region. 相似文献
12.
浙江省永嘉县滑坡灾害危险性区划 总被引:7,自引:0,他引:7
永嘉县是浙江省滑坡灾害发生频繁的区县之一,其滑坡受地质、地形和人类工程活动等因素的影响。本文根据永嘉县滑坡灾害分布情况,选择了影响滑坡分布的主要因素,将各种因子归一化处理后转换成相同分辨率的定量数据,选择了逻辑回归分析模型和信息量模型进行滑坡灾害危险性评价。在逻辑回归模型中,利用SPSS软件,通过逐步回归分析筛选出影响滑坡的最直接的因子,计算出各个因子的回归系数,得到逻辑回归方程,据此编制了危险性预测分区图。在信息量模型中,通过MAPGIS软件及其二次开发的信息量模型,对永嘉县滑坡灾害进行了危险性区划,并依信息量法的结果编制了该区的危险性预测分区图。两种方法所编制的危险性分区图中高危险区和中危险区重合率达到了87%,具有很高的一致性,起到了相互验证的作用,为滑坡的有效防治提供了依据。最后根据"云娜"台风期间永嘉县实际灾害发生情况的资料分析,新灾害点绝大部分落在危险性预测区中的高危险区,表明模型的预测准确率很高。 相似文献
13.
为了弥补滑坡灾害危险性区划研究中影响因子和等级划分的不确定性,结合前人研究成果,依据斜坡几何形态、岩性、地质构造、河流侵蚀、土地利用类型、人类工程活动、降水条件等影响因子与研究区实际已发生的滑坡灾害数之间的关系,编制重庆市万州区滑坡灾害危险性评价标准,并基于GIS技术和信息量模型法,计算滑坡评价因子的信息量,就万州区滑坡危险性进行区划,最后基于乡镇行政区对该区滑坡危险性区划进行细化。结果表明:建设用地、坡高为90~200 m的地形、1 024~1 060 mm的年降雨量以及侏罗系中统上沙溪庙组岩层等因素对万州区滑坡发生影响较大;根据滑坡灾害危险性评价标准,万州区滑坡灾害被划分为高、中、低、极低等4个危险区;应用信息量模型法得到的万州区滑坡危险性区划与实际情况比较吻合;高危险区和中危险区面积分别为564.4 km2和848.6 km2,分别占万州区总面积的16.3%和24.5%,主要分布于长江干流及支流两岸的居民相对集中区以及公路干线地段;高危险和中危险乡镇主要分布在万州区经济较为发达的长江干流两岸,尤其是左岸的黄柏乡、太龙镇、天城镇、李河镇等以及万州主城区。 相似文献
14.
Krishna Chandra Devkota Amar Deep Regmi Hamid Reza Pourghasemi Kohki Yoshida Biswajeet Pradhan In Chang Ryu Megh Raj Dhital Omar F. Althuwaynee 《Natural Hazards》2013,65(1):135-165
Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling?CNarayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75?%) were randomly selected for building landslide susceptibility models, while the remaining 80 (25?%) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16?%. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57?% of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80?% accuracy (i.e. 89.15?% for IOE model, 89.10?% for LR model and 87.21?% for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling?CNarayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. 相似文献
15.
This paper presents a methodology for developing a landslide hazard zonation map by integration of global positioning system
(GPS), geographic information system (GIS), and remote sensing (RS) for Western Himalayan Kaghan Valley of Pakistan. The landslides
in the study area have been located and mapped by using GPS. Eleven causative factors such as landuse, elevation, geology,
rainfall intensity, slope inclination, soil, slope aspect, distances from main road, distances from secondary roads, and distances
from main river and those from trunk streams were analyzed for occurrence of landslides. These factors were used with a modified
form of pixel-based information value model to obtain landslide hazard zones. The matrix analysis was performed in remote
sensing to produce a landslide hazard zonation map. The causative factors with the highest effect of landslide occurrence
were landuse, rainfall intensity, distances from main road, distances from secondary roads, and distances from main river
and those from trunk streams. In conclusion, we found that landslide occurrence was only in moderate, high, or very high hazard
zones, and no landslides were in low or very low hazard zones showing 100% accuracy of our results. The landslide hazard zonation
map showed that the current main road of the valley was in the zones of high or very high hazard. Two new safe road routes
were suggested by using the GIS technology. 相似文献
16.
A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in
an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative
or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic
regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary
Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression
analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and
landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic
regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients
of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for
the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve
analysis, which has given 91.7% accuracy for the developed BLR model. 相似文献
17.
Tirthankar Basu Swades Pal 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2018,12(1):14-28
The occurrence of landslide in the hilly region of Darjeeling during monsoon season is a matter of serious concern. Every year this natural hazard damages the major roads at several places and thus disrupts the transport and communication system in this region. This paper tries to prepare a landslide susceptibility zone (LSZ) map for the Gish River basin. A total number of 16 spatial parameters have been taken for this study and these are categorised under six factor clusters or groups for example, triggering factors, protective factor, lithological factors, morphometric factors, hydrological factors and anthropogenic factors. The LSZ map is prepared by integrating all the parameters adopting the weighting base as logistic regression. The landslide susceptibility map shows that nearly 9.11% of the area falls under the very high landslide-susceptible zone while 40.28% of the area of the total basin lies under the very low landslide-susceptible zone. The landslide-susceptible model is validated through the receiver operating characteristic curve. This curve shows 86% success rate in defining landslide-susceptible zones and 83.40% prediction rate for the occurrence of landslides. The spatial relationship between the landslide susceptibility model and other factors’ groups shows that the morphometric factors’ cluster (mainly slope) is the focalone for the determination of landslide-susceptible zone. 相似文献
18.
Garhwal Himalayas are seismically very active and simultaneously suffering from landslide hazards. Landslides are one of the most frequent natural hazards in Himalayas causing damages worth more than one billion US$ and around 200 deaths every year. Thus, it is of paramount importance to identify the landslide causative factors to study them carefully and rank them as per their influence on the occurrence of landslides. The difference image of GIS-derived landslide susceptibility zonation maps prepared for pre- and post-Chamoli earthquake shows the effect of seismic shaking on the occurrence of landslides in the Garhwal Himalaya. An attempt has been made to incorporate seismic shaking parameters in terms of peak ground acceleration with other static landslide causative factors to produce landslide susceptibility zonation map in geographic information system environment. In this paper, probabilistic seismic hazard analysis has been carried out to calculate peak ground acceleration values at different time periods for estimating seismic shaking conditions in the study area. Further, these values are used as one of the causative factors of landslides in the study area and it is observed that it refines the preparation of landslide susceptibility zonation map in seismically active areas like Garhwal Himalayas. 相似文献
19.
浙江省永嘉县区域滑坡灾害人口易损性评价和伤亡风险预测 总被引:1,自引:0,他引:1
区域滑坡灾害人口易损性及人口伤亡风险预测研究是区域滑坡灾害预警预报工作的一个重要环节,该研究对提高预警预报工作的针对性和有效性具有关键作用.在对浙江省永嘉县有关资料进行分析的基础上,从研究区人口年龄结构、居民对滑坡灾害风险的防范意识、政府对滑坡灾害的重视程度及滑坡灾害预警预报体系的完善程度4个方面评价了研究区人口易损性,并给出了计算人口易损性的公式,据此得到了永嘉县人口易损性分布图.根据永嘉县的实际情况,提出了耕地人口密度的概念.综合人口易损性分布图、人口密度分布图和滑坡灾害易发性预测图得到了研究区受威胁人口伤亡风险预测图,为当地政府职能部门实施滑坡灾害风险的控制和管理提供决策依据. 相似文献
20.
A logistic regression model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards
in a mountainous environment. A case study is conducted in the mountainous southern Mackenzie Valley, Northwest Territories,
Canada. To determine the factors influencing landslides, data layers of geology, surface materials, land cover, and topography
were analyzed by logistic regression analysis, and the results are used for landslide hazard mapping. In this study, bedrock,
surface materials, slope, and difference between surface aspect and dip direction of the sedimentary rock were found to be
the most important factors affecting landslide occurrence. The influence on landslides by interactions among geologic and
geomorphic conditions is also analyzed, and used to develop a logistic regression model for landslide hazard mapping. The
comparison of the results from the model including the interaction terms and the model not including the interaction terms
indicate that interactions among the variables were found to be significant for predicting future landslide probability and
locating high hazard areas. The results from this study demonstrate that the use of a logistic regression model within a GIS
framework is useful and suitable for landslide hazard mapping in large mountainous geographic areas such as the southern Mackenzie
Valley. 相似文献