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1.
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   

2.
This paper introduces a new statistical method that we recommend should become standard procedure to quantify the goodness-of-fit of calibration and validation for land-use change models. We present a multiple-resolution Relative Operating Characteristic (ROC) that measures the goodness-of-fit between a reference map, which is considered reality, and a simulated map, which is a model’s output. This proposed ROC is based on: (1) multiple-resolutions, (2) soft classification, (3) sampling without replacement, and 4) explicit separation of factors of quantity versus location of land-use change. We illustrate the method with a case study in India’s Western Ghats, a biodiversity hotspot, where we have maps of cumulative forest disturbance for 1920 and 1990. We use a predictive modeling approach similar to GEOMOD in which we calibrate the model with the map of 1920, then predict the map of 1990, at which point we subject the model to validation. We show that the fit of calibration tends to be much larger than the fit of validation. Thus if a modeler assess a model by goodness-of-fit of calibration only, then the modeler will likely be over confident in the model’s predictive ability.  相似文献   

3.
The Yushu County, Qinghai Province, China, April 14, 2010, earthquake triggered thousands of landslides in a zone between 96°20′32.9″E and 97°10′8.9″E, and 32°52′6.7″N and 33°19′47.9″N. This study examines the use of geographic information system (GIS) technology and Bayesian statistics in creating a suitable landslide hazard-zone map of good predictive power. A total of 2,036 landslides were interpreted from high-resolution aerial photographs and multi-source satellite images pre- and post-earthquake, and verified by selected field checking before a final landslide-inventory map of the study area could be established using GIS software. The 2,036 landslides were randomly partitioned into two subsets: a training dataset, which contains 80 % (1,628 landslides), for training the model; and a testing dataset 20 % (408 landslides). Twelve earthquake triggered landslide associated controlling parameters, such as elevation, slope gradient, slope aspect, slope curvature, topographic position, distance from main surface ruptures, peak ground acceleration, distance from roads, normalized difference vegetation index, distance from drainages, lithology, and distance from all faults were obtained from variety of data sources. Landslide hazard indices were calculated using the weight of evidence model. The landslide hazard map was compared with training data and testing data to obtain the success rate and predictive rate of the model, respectively. The validation results showed satisfactory agreement between the hazard map and the existing landslide distribution data. The success rate is 80.607 %, and the predictive rate is 78.855 %. The resulting landslide hazard map showed five classes of landslide hazard, i.e., very high, high, moderate, low and very low. The landslide hazard evaluation map should be useful for environmental recovery planning and reconstruction work.  相似文献   

4.
滑坡灾害持续影响着人民生命财产安全和地区社会经济可持续发展,滑坡危险性评价能够为防灾减灾和区域规划提供有效的理论依据。以福建省南平市为研究区,区内1711个历史滑坡灾害点,选择高程、坡度、坡向、曲率、地质岩性、土壤类型、降雨、水系、土地利用类型、公路和铁路共11个影响因子构成基本评价体系。使用Spearman相关系数对各因子进行共线性分析。基于1711个滑坡样本和1711个随机选取的非滑坡样本数据,利用人工神经网络模型对研究区进行了滑坡危险性评价,并利用混淆矩阵和接收者操作特征曲线(ROC)对模型进行验证。结果表明:混淆矩阵精度84.91%,ROC曲线下面积AUC值0.93,说明模型具有较高精度和预测率。使用自然间断法将滑坡危险性分为5个等级,结果表明研究区内危险性最高地区位于延平区和浦城县,顺昌县和松溪县次之,其余地区多为低危险区和较低危险区。研究结果可为当地区域规划和防灾减灾工程提供一定的理论依据和科学指导。  相似文献   

5.
The aim of this study is to analyze the susceptibility conditions to gully erosion phenomena in the Magazzolo River basin and to test a method that allows for driving the factors selection. The study area is one of the largest (225 km2) watershed of southern Sicily and it is mostly characterized by gentle slopes carved into clayey and evaporitic sediments, except for the northern sector where carbonatic rocks give rise to steep slopes. In order to obtain a quantitative evaluation of gully erosion susceptibility, statistical relationships between the spatial distributions of gullies affecting the area and a set of twelve environmental variables were analyzed. Stereoscopic analysis of aerial photographs dated 2000, and field surveys carried out in 2006, allowed us to map about a thousand landforms produced by linear water erosion processes, classifiable as ephemeral and permanent gullies. The linear density of the gullies, computed on each of the factors classes, was assumed as the function expressing the susceptibility level of the latter. A 40-m digital elevation model (DEM) prepared from 1:10,000-scale topographic maps was used to compute the values of nine topographic attributes (primary: slope, aspect, plan curvature, profile curvature, general curvature, tangential curvature; secondary: stream power index; topographic wetness index; LS-USLE factor); from available thematic maps and field checks three other physical attributes (lithology, soil texture, land use) were derived. For each of these variables, a 40-m grid layer was generated, reclassifying the topographic variables according to their standard deviation values. In order to evaluate the controlling role of the selected predictive variables, one-variable susceptibility models, based on the spatial relationships between each single factor and gullies, were produced and submitted to a validation procedure. The latter was carried out by evaluating the predictive performance of models trained on one half of the landform archive and tested on the other. Large differences of accuracy were verified by computing geometric indexes of the validation curves (prediction and success rate curves; ROC curves) drawn for each one-variable model; in particular, soil texture, general curvature and aspect demonstrated a weak or a null influence on the spatial distribution of gullies within the studied area, while, on the contrary, tangential curvature, stream power index and plan curvature showed high predictive skills. Hence, predictive models were produced on a multi-variable basis, by variously combining the one-variable models. The validation of the multi-variables models, which generally indicated quite satisfactory results, were used as a sensitivity analysis tool to evaluate differences in the prediction results produced by changing the set of combined physical attributes. The sensitivity analysis pointed out that by increasing the number of combined environmental variables, an improvement of the susceptibility assessment is produced; this is true with the exception of adding to the multi-variables models a variable, as slope aspect, not correlated to the target variable. The addition of this attribute produces effects on the validation curves that are not distinguishable from noise and, as a consequence, the slope aspect was excluded from the final multi-variables model used to draw the gully erosion susceptibility map of the Magazzolo River basin. In conclusion, the research showed that the validation of one-variable models can be used as a tool for selecting factors to be combined to prepare the best performing multi-variables gully erosion susceptibility model.  相似文献   

6.
The determining of landslide-prone areas in mountainous terrain is essential for land planning and hazard mitigation. In this paper, a comparative study using three statistical models including weight of evidence model (WoE), logistic regression model (LR) and support vector machine method (SVM) was undertaken in the Zhouqu to Wudu segment in the Bailong River Basin, Southern Gansu, China. Six conditionally independent environmental factors, elevation, slope, aspect, distance from fault, lithology and settlement density, were selected as the explanatory variables that may contribute to landslide occurrence based on principal component analysis (PCA) and Chi-square test. The relation between landslide distributions and these variables was analyzed using the three models and the results then used to calculate the landslide susceptibility (LS). The performance of the models was then evaluated using both the highly accurate deformation signals produced by using the Small Baseline Subset Interferometric Synthetic Aperture Radar technique and Receiver Operating Characteristic (ROC) curve. Results show more deformation points in areas with high and very high LS levels, and also more stable points in areas with low and very low LS levels for the SVM model. In addition, the SVM has larger area under the ROC curve. It indicates that the SVM has better prediction accuracy and classified ability. For the interpretability, the WoE derives the class of factors that most contributed to landsliding in the study area, and the LR reveals that factors including elevation, settlement density and distance from fault played major roles in landslide occurrence and distribution, whereas the SVM cannot provide relative weights for the variables. The outperformed SVM could be employed to determine potential landslide zones in the study area. Outcome of this research would provide preliminary basis for general land planning such as choosing new urban areas and infrastructure construction in the future, as well as for landslide hazard mitigation in Bailong River Basin.  相似文献   

7.
Applied flood risk analyses, especially in urban areas, very often pose the question how detailed the analysis needs to be in order to give a realistic figure of the expected risk. The methods used in research and practical applications range from very basic approaches with numerous simplifying assumptions up to very sophisticated, data and calculation time demanding applications both on the hazard and on the vulnerability part of the risk. In order to shed some light on the question of required model complexity in flood risk analyses and outputs sufficiently fulfilling the task at hand, a number of combinations of models of different complexity both on the hazard and on the vulnerability side were tested in a case study. The different models can be organized in a model matrix of different complexity levels: On the hazard side, the approaches/models selected were (A) linear interpolation of gauge water levels and intersection with a digital elevation model (DEM), (B) a mixed 1D/2D hydraulic model with simplifying assumptions (LISFLOOD-FP) and (C) a Saint-Venant 2D zero-inertia hyperbolic hydraulic model considering the built environment and infrastructure. On the vulnerability side, the models used for the estimation of direct damage to residential buildings are in order of increasing complexity: (I) meso-scale stage-damage functions applied to CORINE land cover data, (II) the rule-based meso-scale model FLEMOps+ using census data on the municipal building stock and CORINE land cover data and (III) a rule-based micro-scale model applied to a detailed building inventory. Besides the inundation depths, the latter two models consider different building types and qualities as well as the level of private precaution and contamination of the floodwater. The models were applied in a municipality in east Germany, Eilenburg. It suffered extraordinary damage during the flood of August 2002, which was well documented as were the inundation extent and depths. These data provide an almost unique data set for the validation of flood risk analyses. The analysis shows that the combination of the 1D/2D model and the meso-scale damage model FLEMOps+ performed best and provide the best compromise between data requirements, simulation effort, and an acceptable accuracy of the results. The more detailed approaches suffered from complex model set-up, high data requirements, and long computation times.  相似文献   

8.
为探索区域地质灾害敏感性评价方法,以宁夏盐池县为研究区域,选取坡度、坡向、坡高、高程、地层、距河流距离、距道路距离、植被覆盖度等8个影响地质灾害发生的评价因子,分别采用信息量模型+逻辑回归模型(I+LR)和确定性系数模型+逻辑回归模型(CF+LR)2种组合模型对盐池县地质灾害敏感性进行评价,将该区域地质灾害划分为极低、低、中和高敏感区4类,并完成结果检验。结果表明:(1)2种组合模型得到的低、中敏感区面积基本相当,而高敏感区面积相差较大,CF+LR模型较I+LR模型高敏感区面积增加约5336 km2,而极低敏感区面积减少约6%;(2)2种组合模型的合理性均符合检验要求,且ROC精度检验AUC值分别为0868和0829,渐进Sigb均小于005,表明2种组合评价模型都能较为客观准确地评价盐池县地质灾害敏感性;(3)ROC检验精度与盐池县地质灾害发育情况均表明I+LR模型精度更高。  相似文献   

9.
Landslides are one of the most frequent and common natural hazards in many parts of Himalaya. To reduce the potential risk, the landslide susceptibility maps are one of the first and most important steps in the landslide hazard mitigation. Earth observation satellite and geographical information system-based techniques have been used to derive and analyse various geo-environmental parameters significant to landslide hazards. In this study, a bivariate statistics method was used for spatial modelling of landslide susceptibility zones. For this purpose, thematic layers including landslide inventory, geology, slope angle, slope aspect, geomorphology, slope morphology, drainage density, lineament and land use/land cover were used. A large number of landslide occurrences have been observed in the upper Tons river valley area of Western Himalaya. The result has been used to spatially classify the study area into zones of very high, high, moderate, low and very low landslide susceptibility zones. About 72% of active landslides have been observed to occur in very high and high hazard zones. The result of the analysis was verified using the landslide location data. The validation result shows significant agreement between the susceptibility map and landslide location. The result can be used to reduce landslide hazards by proper planning.  相似文献   

10.
Proxy reconstructions of climatic parameters developed using transfer functions are central to the testing of many palaeoclimatic hypotheses on Holocene timescales. However, recent work shows that the mathematical models underpinning many existing transfer functions are susceptible to spatial autocorrelation, clustered training set design and the uneven sampling of environmental gradients. This may result in over‐optimistic performance statistics or, in extreme cases, a lack of predictive power. A new testate amoeba‐based transfer function is presented that fully incorporates the new recommended statistical tests to address these issues. Leave‐one‐out cross‐validation, the most commonly applied method in recent studies to assess model performance, produced over‐optimistic performance statistics for all models tested. However, the preferred model, developed using weighted averaging with tolerance downweighting, retained a predictive capacity equivalent to other published models even when less optimistic performance statistics were chosen. Application of the new statistical tests in the development of transfer functions provides a more thorough assessment of performance and greater confidence in reconstructions based on them. Only when the wider research community have sufficient confidence in transfer function‐based proxy reconstructions will they be commonly used in data comparison and palaeoclimate modelling studies of broader scientific relevance. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

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