共查询到20条相似文献，搜索用时 322 毫秒
1.
The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks 总被引：4，自引：2，他引：2
Işık Yilmaz 《Environmental Earth Sciences》2010,60(3):505-519
This study presented herein compares the effect of the sampling strategies by means of landslide inventory on the landslide
susceptibility mapping. The conditional probability (CP) and artificial neural networks (ANN) models were applied in Sebinkarahisar
(Giresun–Turkey). Digital elevation model was first constructed using a geographical information system software and parameter
maps affecting the slope stability such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect,
topographic wetness index, stream power index and normalized difference vegetation index were considered. In the last stage
of the analyses, landslide susceptibility maps were produced applying different sampling strategies such as; scarp, seed cell
and point. The maps elaborated were then compared by means of their validations. Scarp sampling strategy gave the best results
than the point, whereas the scarp and seed cell methods can be evaluated relatively similar. Comparison of the landslide susceptibility
maps with known landslide locations indicated that the higher accuracy was obtained for ANN model using the scarp sampling
strategy. The results obtained in this study also showed that the CP model can be used as a simple tool in assessment of the
landslide susceptibility, because input process, calculations and output process are very simple and can be readily understood. 相似文献
2.
Landslide-related factors were extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, and integrated techniques were developed, applied, and verified for the analysis of landslide susceptibility in Boun, Korea, using a geographic information system (GIS). Digital elevation model (DEM), lineament, normalized difference vegetation index (NDVI), and land-cover factors were extracted from the ASTER images for analysis. Slope, aspect, and curvature were calculated from a DEM topographic database. Using the constructed spatial database, the relationships between the detected landslide locations and six related factors were identified and quantified using frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) models. These relationships were used as factor ratings in an overlay analysis to create landslide susceptibility indices and maps. Three landslide susceptibility maps were then combined and applied as new input factors in the FR, LR, and ANN models to make improved susceptibility maps. All of the susceptibility maps were verified by comparison with known landslide locations not used for training the models. The combined landslide susceptibility maps created using three landslide-related input factors showed improved accuracy (87.00% in FR, 88.21% in LR, and 86.51% in ANN models) compared to the individual landslide susceptibility maps (84.34% in FR, 85.40% in LR, and 74.29% in ANN models) generated using the six factors from the ASTER images. 相似文献
3.
Vahid Nourani Biswajeet Pradhan Hamid Ghaffari Seyed Saber Sharifi 《Natural Hazards》2014,71(1):523-547
Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices. 相似文献
4.
5.
基于GIS与ANN模型的地震滑坡易发性区划 总被引：1，自引：0，他引：1
基于遥感数据、地理信息系统(GIS)技术和人工神经网络(ANN)模型,开展地震滑坡易发性区划研究.2010年4月14日玉树地震后,基于航片与卫星影像目视解译,并辅以野外调查的方法,在地震区圈定了2036处地震诱发滑坡.选择高程、坡度、坡向、斜坡曲率、坡位、与水系距离、地层岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)、与同震地表破裂距离、地震动峰值加速度(PGA)共12个因子作为地震滑坡易发性评价因子.这些因子均是应用GIS技术与遥感影像处理技术,基于地形数据、地质数据、遥感数据得到.训练样本中的滑动样本有两组,一组是滑坡区整个单滑坡体的质心位置,另一组是滑坡滑源区滑前的坡体高程最高的位置.应用这12个影响因子,分别采用这两组评价样本,基于ANN模型建立地震滑坡易发性索引图,基于GIS工具建立地震滑坡易发性分级图.分别应用训练样本中滑坡分布的点数据去检验各自的结果正确率,正确率分别为81.53％与81.29％,表明ANN模型是一种高效科学的地震滑坡易发性区划模型. 相似文献
6.
H. R. Pourghasemi H. R. Moradi S. M. Fatemi Aghda C. Gokceoglu B. Pradhan 《Arabian Journal of Geosciences》2014,7(5):1857-1878
The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWIS-GIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multi-criteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. 相似文献
7.
Landslide susceptibility assessment forms the basis of any hazard mapping, which is one of the essential parts of quantitative
risk mapping. For the same study area, different susceptibility maps can be achieved depending on the type of susceptibility
mapping methods, mapping unit, and scale. Although there are various methods of obtaining susceptibility maps, the efficiency
and performance of each method should be evaluated. In this study the effect of mapping unit and susceptibility mapping method
on landslide susceptibility assessment is investigated. When analyzing the effect of susceptibility mapping method, logistic
regression (LR) which is widely used in landslide susceptibility mapping and, spatial regression (SR), which have not been
used for landslide susceptibility mapping, are selected. The susceptibility maps with logistic and spatial regression models
are obtained using two different mapping units namely slope unit-based and grid-based mapping units. The procedure for investigation
of effect of mapping unit on different susceptibility mapping methods is applied to Kumluca watershed, in Bartin Province
of Western Black Sea Region, Turkey. 18 factor maps are prepared for landslide susceptibility assessment in the study region.
Geographic information systems and remote sensing techniques are used to create the landslide factor maps, to obtain susceptibility
maps and to compare the results. The relative operating characteristics (ROC) curve is used to compare the predictive abilities
of each model and mapping unit and also the accuracy is evaluated depending on the observations made during field surveys.
By analyzing the area under the ROC curve for grid-based and slope unit-based mapping units, it can be concluded that SR model
provide better predictive performance (0.774 in grids and 0.898 in slope units) as compared to the LR model (0.744 in grids
and 0.820 in slope units). This result is also supported by the accuracy analysis. For both mapping units, the SR model provides
more accurate result (0.55 for grids and 0.57 for slope units) than the LR model (0.50 for grids and 0.48 for slopes). The
main reason for this better performance is that the spatial correlations between the mapping units are incorporated into the
model in SR while this fact is not considered in LR model. 相似文献
8.
Binh Thai Pham Dieu Tien Bui Indra Prakash Long Hoang Nguyen M. B. Dholakia 《Environmental Earth Sciences》2017,76(10):371
Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas. 相似文献
9.
Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate?=?80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures. 相似文献
10.
Zhiyong Wu Yanli Wu Yitian Yang Fuwei Chen Na Zhang Yutian Ke Wenping Li 《Arabian Journal of Geosciences》2017,10(8):187
The logistic regression and statistical index models are applied and verified for landslide susceptibility mapping in Daguan County, Yunnan Province, China, by means of the geographic information system (GIS). A detailed landslide inventory map was prepared by literatures, aerial photographs, and supported by field works. Fifteen landslide-conditioning factors were considered: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, STI, SPI, and TWI were derived from digital elevation model; NDVI was extracted from Landsat ETM7; rainfall was obtained from local rainfall data; distance to faults, distance to roads, and distance to rivers were created from a 1:25,000 scale topographic map; the lithology was extracted from geological map. Using these factors, the landslide susceptibility maps were prepared by LR and SI models. The accuracy of the results was verified by using existing landslide locations. The statistical index model had a predictive rate of 81.02%, which is more accurate prediction in comparison with logistic regression model (80.29%). The models can be used to land-use planning in the study area. 相似文献
11.
Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping 总被引：3，自引：0，他引：3
Landslides are recognized as one of the most important natural hazards in many areas throughout the world. Producing landslide susceptibility maps have received particular attention from a wide range of scientists. The main objective of this study was to produce landslide susceptibility maps using hybrid wavelet packet-statistical models (WP-SM). In the first step, landslide susceptibility maps were produced using single artificial neural network (ANN), support vector machine (SVM), maximum entropy (MaxEnt), and generalized linear model (GLM). In the next step, the input maps were preprocessed using different mother wavelets in different levels. Then, the hybrid models were developed using the wavelet-based preprocessed maps. Results showed that the wavelet packet transform can be effectively used to produce precise landslide susceptibility maps. It was shown that wavelet packet transform significantly enhanced the ability of the single statistical models. The kappa coefficients were increased from 0.829 to 0.941, 0.846 to 0.978, 0.744 to 0.829, and 0.735 to 0.817 in hybrid ANN, SVM, MaxEnt, and GLM, respectively. The best wavelet transform was performed using bior1.5 with a three-level decomposition. It was also recognized that MaxEnt and GLM produced approximately poor results. However, SVM performed better than the other three models both in single and hybrid forms. ANN also outperformed MaxEnt and GLM models. Spatial distribution of the susceptible area is consistent with the observed landslide distribution pattern particularly in maps obtained from the hybrid models. The produced maps showed that the general pattern of susceptible area intensively followed the pattern of roads and sensitive geological formations. 相似文献
12.
Samaneh Razavizadeh Karim Solaimani Matteo Massironi Ataollah Kavian 《Environmental Earth Sciences》2017,76(14):499
The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies. 相似文献
13.
Soyoung Park Chuluong Choi Byungwoo Kim Jinsoo Kim 《Environmental Earth Sciences》2013,68(5):1443-1464
Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619). 相似文献
14.
Chong Xu Xiwei Xu Fuchu Dai Zhide Wu Honglin He Feng Shi Xiyan Wu Suning Xu 《Natural Hazards》2013,68(2):883-900
The main purpose of this paper is to present the use of multi-resource remote sensing data, an incomplete landslide inventory, GIS technique and logistic regression model for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Landslide location polygons were delineated from visual interpretation of aerial photographs, satellite images in high resolutions, and verified by selecting field investigations. Eight factors, including slope angle, slope aspect, elevation, distance from drainages, distance from roads, distance from main faults, seismic intensity and lithology were selected as controlling factors for earthquake-triggered landslide susceptibility mapping. Qualitative susceptibility analyses were carried out using the map overlaying techniques in GIS platform. The validation result showed a success rate of 82.751 % between the susceptibility probability index map and the location of the initial landslide inventory. The predictive rate of 86.930 % was obtained by comparing the additional landslide polygons and the landslide susceptibility probability index map. Both the success rate and the predictive rate show sufficient agreement between the landslide susceptibility map and the existing landslide data, and good predictive power for spatial prediction of the earthquake-triggered landslides. 相似文献
15.
The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection. 相似文献
16.
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
17.
Juraj Holec Martin Bednarik Marián Šabo Jozef Minár Isik Yilmaz Marián Marschalko 《Natural Hazards》2013,69(1):1081-1107
This study presented herein compares the bivariate and multivariate landslide susceptibility mapping methods and presents the landslide susceptibility map of the territory of Western Carpathians in small scale. This study also describes pioneer work for the territory of Western Carpathians, overreaching state borders, using verified sophisticated statistical methods. In the susceptibility mapping, digital elevation model was first constructed using a GIS software, and parameter maps affecting the slope stability such as geology, seismicity, precipitation, topographical elevation, slope angle, slope aspect and land cover were considered. In the last stage of the analyses, landslide susceptibility maps were produced using bivariate and multivariate analyses, and they were then compared by means of their validations. The validation of the bivariate analysis data was performed using the results of bivariate analysis for landslide areas of Slovakia containing five classes of susceptibility in scale 1:500,000. The validation area is the area of Western Carpathians within Slovakia. Eighty-two per cent of area does not differ in more than one class. The validation of the multivariate analysis data was performed using the results from the Kysuce region in the northern part of Slovakia in scale 1:10,000. The raster calculator was used to express the difference between each pair of pixels within these two layers. Seventy-seven per cent of the pixels do not differ in more than 25 %, 94 % of the pixels do not differ in more than 50 %. The maximal possible difference is 100 % (one pixel with value 0 and other with value 1, or vice versa). Receiver operating characteristic analysis was also performed, the area under curve value for bivariate model was calculated to be 0.735, while it was 0.823 for multivariate. The results of the validation can be considered as satisfactory. 相似文献
18.
Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide
occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR)
ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment.
However, to assess the probability of landslide within a specified period of time and within a given area, it is important
to understand the spatial correlation between landslide occurrences and influencing factors. By including these relations,
the predictive ability of the developed model increases. In this respect, spatial regression (SR) and geographically weighted
regression (GWR) techniques, which consider spatial variability in the parameters, are proposed in this study for landslide
hazard assessment to provide better realistic representations of landslide susceptibility. The proposed model was implemented
to a case study area from More and Romsdal region of Norway. Topographic (morphometric) parameters (slope angle, slope aspect,
curvature, plan, and profile curvatures), geological parameters (geological formations, tectonic uplift, and lineaments),
land cover parameter (vegetation coverage), and triggering factor (precipitation) were considered as landslide influencing
factors. These influencing factors together with past rock avalanche inventory in the study region were considered to obtain
landslide susceptibility maps by using SR and LR models. The comparisons of susceptibility maps obtained from SR and LR show
that SR models have higher predictive performance. In addition, the performances of SR and LR models at the local scale were
investigated by finding the differences between GWR and SR and GWR and LR maps. These maps which can be named as comparison
maps help to understand how the models estimate the coefficients at local scale. In this way, the regions where SR and LR
models over or under estimate the landslide hazard potential were identified. 相似文献
19.
GIS-based groundwater spring potential assessment and mapping in the Birjand Township,southern Khorasan Province,Iran 总被引：5，自引：1，他引：4
Three statistical models—frequency ratio (FR), weights-of-evidence (WofE) and logistic regression (LR)—produced groundwater-spring potential maps for the Birjand Township, southern Khorasan Province, Iran. In total, 304 springs were identified in a field survey and mapped in a geographic information system (GIS), out of which 212 spring locations were randomly selected to be modeled and the remaining 92 were used for the model evaluation. The effective factors—slope angle, slope aspect, elevation, topographic wetness index (TWI), stream power index (SPI), slope length (LS), plan curvature, lithology, land use, and distance to river, road, fault—were derived from the spatial database. Using these effective factors, groundwater spring potential was calculated using the three models, and the results were plotted in ArcGIS. The receiver operating characteristic (ROC) curves were drawn for spring potential maps and the area under the curve (AUC) was computed. The final results indicated that the FR model (AUC?=?79.38 %) performed better than the WofE (AUC?=?75.69 %) and LR (AUC?=?63.71 %) models. Sensitivity and factor analyses concluded that the bivariate statistical index model (i.e. FR) can be used as a simple tool in the assessment of groundwater spring potential when a sufficient number of data are obtained. 相似文献
20.
Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu,Turkey) 总被引：6，自引：4，他引：2
Landslides and their assessments are of great importance since they damage properties, infrastructures, environment, lives
and so on. Particularly, landslide inventory, susceptibility, and hazard or risk mapping have become important issues in the
last few decades. Such maps provide useful information and can be produced by qualitative or quantitative methods. The work
presented in this paper aimed to assess landslide susceptibility in a selected area, covering 570.625 km2 in the Western Black Sea region of Turkey, by two quantitative methods. For this purpose, in the first stage, a detailed
landslide inventory map was prepared by extensive field studies. A total of 96 landslides were mapped during these studies.
To perform landslide susceptibility analyses, six input parameters such as topographical elevation, lithology, land use, slope,
aspect and distance to streams were considered. Two quantitative methods, logistic regression and fuzzy approach, were used
to assess landslide susceptibility in the selected area. For the fuzzy approach, the fuzzy and, or, algebraic product, algebraic
sum and gamma operators were considered. At the final stage, 18 landslide susceptibility maps were produced by the logistic
regression and fuzzy operators in a GIS (Geographic Information System) environment. Two performance indicators such as ROC
(relative operating characteristics) and cosine amplitude method (r
ij
) were used to validate the final susceptibility maps. Based on the analyses, the landslide susceptibility map produced by
the fuzzy gamma operator with a level of 0.975 showed the best performance. In addition, the maps produced by the logistic
regression, fuzzy algebraic product and the higher levels of gamma operators showed more satisfactory results, while the fuzzy
and, or, algebraic sum maps were not sufficient to provide reliable outputs. 相似文献