using multiple logistic regression and gis technology to predict landslide hazard in northeast kansas usa abstract landslides in the hilly terrain along the kansas and missouri rivers in northeastern kansas have caused millions of dollars in property damage during the last decade to address this problem a statistical method called multiple logistic regression has been used to create a landslide hazard map for atchison kansas and surrounding areas data included digitized geology slopes and landslides manipulated using arcview gis logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides given local slopes and geologic units results indicated that slope is the most important variable for estimating landslide hazard in the study area geologic units consisting mostly of shale siltstone and sandstone were most susceptible to landslides soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression soil types were highly correlated with the geologic units and no significant relationships existed between landslides and slope aspect

evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the turbolo river catchment northern calabria italy abstract landslides are one of the most widespread natural hazards that cause damage to both property and life every year and therefore the spatial distribution of the landslide susceptibility is necessary for planning future developmental activities in this paper the artificial neural network ann technique is tested for developing a landslide susceptibility map in turbolo river catchment north calabria south italy landslides were mapped through air photo interpretation and field surveys by identifying both the landslide depletion zones dzs and accumulation zones azs and relevant geo environmental thematic layers pertaining to landslide predisposing factors were generated using air photo interpretation field surveys and geographic information system gis tools ten predisposing factors were related to the occurrence of landslide lithology faults land use drainage network and a series of topographic factors elevation slope aspect plan curvature topographic wetness index twi and stream power index spi in order to evaluate and validate landslide susceptibility the dzs were divided in two groups using a random partition strategy the first group training set was used to prepare the susceptibility map employing a back propagation learning algorithm in the idrisi taiga software the second group testing set was used to validate the landslide susceptibility model using the confusion matrix and the receiver operating characteristic roc curve the susceptibility map was classified into five susceptibility classes very low low moderate high and very high about 46 of the study area falls in high to very high susceptible classes and most of the dzs mapped 87 3 occur in the same classes the validation results showed satisfactory agreement between the susceptibility map and the dzs locations over 85 of the dzs of the validation set are correctly classified falling in high and very high susceptibility areas also the roc curve had shown an area under curve auc value of 0 90 which demonstrates the robustness and good reliability of the landslide susceptibility model according to these results we conclude that the map produced by the artificial neural network is reliable and the methodology applied in the study produced high performance and satisfactory results which may be useful for land planning policy

application of lidar and discriminant analysis to determine landscape characteristics for different types of slope failures in heavily vegetated steep terrain horseshoe run watershed west virginia abstract a landslide inventory map was created for horseshoe run watershed west virginia using high resolution topographic data obtained from airborne lidar a total of 152 landslides were remotely mapped and classified as planar slides rotational slumps debris flows debris fans debris slides and active slopes based on morphologic characteristics interpreted from lidar derived shaded relief maps with field verification at several locations seven landscape variables were calculated for the watershed from the high resolution 0 5 m topographic data elevation slope angle slope aspect distance from roads distance from streams plan curvature and profile curvature statistical analyses were conducted to determine the mean and standard deviations for each variable and corresponding slope failure type discriminant analyses were conducted on the different types of failed slopes with a maximum 86 accuracy level to determine which landscape characteristics contribute to each type of landslide results from the discriminant analyses indicate that statistically significant differences exist between the failure classes mapped within the watershed and that slope angle slope aspect and landscape curvature are important variables influencing the type of process the results from this study have important implications for predicting where certain types of slope failures may occur based on landscape characteristics

gis techniques and statistical models in evaluating landslide hazard geographical information systems gis and numerical cartography may greatly facilitate the development and use of statistical models for the assessment of regional landslide hazard from a small drainage basin located in central italy relevant geological and geomorphological factors were collected and processed by applying gis technology in particular modules were used which both generate high fidelity digital terrain models and automatically partition the terrain into main slope units the resulting information was then analysed by discriminant analysis which enabled landslide hazard and risk to be evaluated in each slope unit although not lacking drawbacks the method proved to be a feasible and cost effective approach to landslide susceptibility assessment and mapping

landslide spatial susceptibility mapping by using gis and remote sensing techniques a case study in zigui county the three georges reservoir china landslides are one of the most destructive phenomena in nature and damage both property and lives every year in this paper a logistic regression model with datasets developed via a geographic information system and remotely sensed data was used to create a landslide spatial susceptibility map for the three gorges project reservoir region on the yangtze river in zigui county the five causative factors used in the logistic regression model were evaluated in different ways topographic slope and topographic aspect were derived from a topographical map at 1 50 000 scale bed rock slope relationship and lithology were obtained from a geological map at 1 50 000 scale and fractional vegetation cover fvc which represents the reduced frequency of landslides due to the vegetation canopy and ground cover and is also one of the most difficult parameters to estimate over broad geographic areas was generated using a back propagation neural network bpnn method based on cbers china brazil earth resources satellite data the results of which were compared with values measured in the field the obtained pearson correlation coefficient r was 0 899 then the fvc factor and the other four factors were used as the input to a logistic regression model by integrating the five factor maps in the geographical information system gis via pixel based computing the landslide spatial susceptibility map was obtained the study area was reclassified into four categories of landslide susceptibility severe moderate low and very low approximately 15 0 of the study area was identified as severe susceptibility and very low low and moderate susceptibility zones covered 21 8 41 7 and 21 5 of the area respectively these results have an accuracy of 78 90 thus by using a logistic regression model in a gis environment a spatial susceptibility map of landslides can be obtained and the regions in zigui county that are susceptible to landslides and need immediate protective and mitigation measures can be identified

a gis based landslide susceptibility evaluation using bivariate and multivariate statistical analyses bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the cuyahoga river watershed northeastern ohio u s a the relationship between landslides and various instability factors contributing to their occurrence was evaluated using a geographic information system gis based investigation a landslide inventory map was prepared using landslide locations identified from aerial photographs field checks and existing literature instability factors such as slope angle soil type soil erodibility soil liquidity index landcover pattern precipitation and proximity to stream responsible for the occurrence of landslides were imported as raster data layers in arcgis and ranked using a numerical scale corresponding to the physical conditions of the region in order to investigate the role of each instability factor in controlling the spatial distribution of landslides both bivariate and multivariate models were used to analyze the digital dataset the logistic regression approach was used in the multivariate model analysis both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method and by comparing the maps with the known landslide locations the multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area the logistic regression model produced a landslide susceptibility map at a scale of 1 24 000 that classified susceptibility into four categories low moderate high and very high the results also indicated that slope angle proximity to stream soil erodibility and soil type were statistically significant in controlling the slope movement

impact of bending related faulting on the seismic properties of the incoming oceanic plate offshore of nicaragua impact of bending related faulting on the seismic properties of the incoming oceanic plate offshore of nicaragua

extraction of potential debris source areas by logistic regression technique a case study from barla besparmak and kapi mountains nw taurids turkey debris flow is one of the most destructive mass movements sometimes regional debris flow susceptibility or hazard assessments can be more difficult than the other mass movements determination of debris accumulation zones and debris source areas which is one of the most crucial stages in debris flow investigations can be too difficult because of morphological restrictions the main goal of the present study is to extract debris source areas by logistic regression analyses based on the data from the slopes of the barla besparmak and kapi mountains in the sw part of the taurids mountain belt of turkey where formation of debris material are clearly evident and common in this study in order to achieve this goal extensive field observations to identify the areal extent of debris source areas and debris material air photo studies to determine the debris source areas and also desk studies including geographical information system gis applications and statistical assessments were performed to justify the training data used in logistic regression analyses as representative a random sampling procedure was applied by using the results of the logistic regression analysis the debris source area probability map of the region is produced however according to the field experiences of the authors the produced map yielded over predicted results the main source of the over prediction is structural relation between the bedding planes and slope aspects on the basis of the field observations for the generation of debris the dip of the bedding planes must be taken into consideration regarding the slope face in order to eliminate this problem in this study an approach has been developed using probability distribution of the aspect values with the application of structural adjustment the final adjusted debris source area probability map is obtained for the study area the field observations revealed that the actual debris source areas in the field coincide with the areas having high probability values on this final map

mammals from the upper cretaceous oldman formation alberta v eodelphis matthew and the evolution of the stagodontidae marsupialia the judithian late cretaceous stagodontid marsupial eodelphis matthew is known from two closely related species e cutleri and e browni which differ in size and proportions of p3 and robustness of the molars p1 is shown to be two rooted in both species and not only in e cutleri as previously believed known upper molars of eodelphis lack a stylar cusp d indicating that neither e cutleri nor e browni can be a part of the direct species ancestry of the later cretaceous stagodontid didelphodon evolution of the stagodontidae probably included derivation of an e cutleri like radicle having robust premolars and molars from e browni like ancestors present in north america by aquilan time development by the edmontonian of specialized crushing premolars in primitive species of didelphodon descended from ancestors that were similar to e cutleri in size but had a prominent stylar cusp d on the upper molars and subsequent increase in size leading to lancian d vorax and d padanicus contrary to

susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses abstract sometimes regional meteorological anomalies trigger different types of mass movements in may 1998 the western black sea region of turkey experienced such a meteorological anomaly numerous residential and agricultural areas and engineering lifelines were buried under the flood waters besides the reactivation of many previously delineated landslides thousands of small scale landslides mostly the earthflow type occurred all over the region the earthflows were mainly developed in flysch type units which have already presented high landslide concentrations in this study three different catchments namely agustu egerci and kelemen were selected because they have the most landslide prone geological units of the region the purposes of the present study are to put forward the spatial distributions of the shallow earthflows triggered to describe the possible factors conditioning the earthflows and to produce the shallow earthflow susceptibility maps of the three catchments the unique condition units ucu were employed during the production of susceptibility maps and during statistical analyses the unique condition units numbered 4052 for the agustu catchment 13 241 for the egerci catchment and 12 314 for the kelemen catchment the earthflow intensity is the highest in the agustu catchment 0 038 flow ucu and lowest in the egerci catchment 0 0035 flow ucu logistic regression analyses were also employed however during the analyses some difficulties were encountered to overcome the difficulties a series of sensitivity analyses were performed based on some decision rules introduced in the present study considering the decision rules the proper ratios of ucu free from earthflow 0 ucu including the earthflow 1 for the agustu egerci and kelemen catchments were obtained as 3 6 and 5 respectively also a chart for the proper ratio selection was developed the regression equations from the selected ratios were then applied to the entire catchment and the earthflow susceptibility maps were produced the landslide susceptibility maps revealed that 15 of the agustu catchment 8 of the egerci catchment and 7 of the kelemen catchment have very high earthflow susceptibility and most of the earthflows triggered by the may 1998 meteorological event were found in the very high susceptibility zones

landslide hazard zonation using remote sensing and gis a case study of dikrong river basin arunachal pradesh india landslides are among the most costly and damaging natural hazards in mountainous regions triggered mainly under the influence of earthquakes and or rainfall in the present study landslide hazard zonation lhz of dikrong river basin of arunachal pradesh was carried out using remote sensing and geographic information system gis various thematic layers namely slope photo lineament buffer thrust buffer relative relief map geology and land use land cover map were generated using remote sensing data and gis the weighting rating system based on the relative importance of various causative factors as derived from remotely sensed data and other thematic maps were used for the lhz the different classes of thematic layers were assigned the corresponding rating value as attribute information in the gis and an attribute map was generated for each data layer each class within a thematic layer was assigned an ordinal rating from 0 to 9 summation of these attribute maps were then multiplied by the corresponding weights to yield the landslide hazard index lhi for each cell using trial and error method the weight rating values have been re adjusted the lhi threshold values used were 142 165 189 and 216 a lhz map was prepared showing the five zones namely very low hazard low hazard moderate hazard high hazard and very high hazard by using the slicing operation

landslide susceptibility map refinement using psinsar data abstract landslide susceptibility maps lsm are commonly used by local authorities for land use management and planning activities representing a valuable tool used to support decision makers in urban and infrastructural planning the accuracy of a landslide susceptibility map is affected by false negative and false positive errors which can decrease the reliability of this useful product in particular false negative errors are generally worse in terms of social and economic losses with respect to the losses associated with false positives in this paper we present a new technique to improve the accuracy of landslide susceptibility maps using permanent scatterer interferometric synthetic aperture radar psinsar data the proposed approach uses two different data sets acquired in ascending and descending geometry the ps velocity measured along the line of sight is re projected into a new velocity along the steepest slope direction v slope integration between the lsm and the ground deformation velocity map along the slope was performed using an empirical contingency matrix which takes into account the average v slope and the susceptibility degree obtained using the random forests algorithm the results show that the susceptibility degree increased in 56 41 km 2 of the study area the combination of psinsar data and the landslide susceptibility map lsm improved the prediction reliability of slow moving landslides which particularly affect urbanized areas the use of this procedure can be easily applied in different areas where psi data sets are available this approach will help planning and decision making authorities produce reliable landslide susceptibility maps correcting some of the lsm errors

landslide susceptibility mapping based on support vector machine a case study on natural slopes of hong kong china abstract the support vector machine svm is an increasingly popular learning procedure based on statistical learning theory and involves a training phase in which the model is trained by a training dataset of associated input and target output values the trained model is then used to evaluate a separate set of testing data there are two main ideas underlying the svm for discriminant type problems the first is an optimum linear separating hyperplane that separates the data patterns the second is the use of kernel functions to convert the original non linear data patterns into the format that is linearly separable in a high dimensional feature space in this paper an overview of the svm both one class and two class svm methods is first presented followed by its use in landslide susceptibility mapping a study area was selected from the natural terrain of hong kong and slope angle slope aspect elevation profile curvature of slope lithology vegetation cover and topographic wetness index twi were used as environmental parameters which influence the occurrence of landslides one class and two class svm models were trained and then used to map landslide susceptibility respectively the resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression lr method it is concluded that two class svm possesses better prediction efficiency than logistic regression and one class svm however one class svm which only requires failed cases has an advantage over the other two methods as only failed case information is usually available in landslide susceptibility mapping

slope control on the frequency distribution of shallow landslides and associated soil properties north island new zealand the extrapolation of results from field trials to larger areas of land for purposes of regional impact assessment is an important issue in geomorphology particularly for landform properties that show high stochastic variability in space and time such as shallow landslide erosion it is shown in this study that by identifying the main driver for spatial variability in shallow landslide erosion at field scales namely slope angle it is possible to develop a set of generic functions for assessing the impact of landslides on selected soil properties at larger spatial scales and over longer time periods research was conducted within an area of pastoral soft rock tertiary hill country in the north island of new zealand that is subject to infrequent high intensity rainfall events producing numerous landslides most of which are smaller than several hundred square metres in size and remove soil to shallow depths all landslides were mapped within a 0 6 km2 area and registered to a high resolution 2 m slope map to show that few landslides occur on slopes 24 the areal density of landslides from all historical events showed an approximately linear increase with slope above 24 integrating landslide densities with soil recovery data demonstrates that the average value of a soil property fluctuates in a saw tooth fashion through time with the overall shape of the curve controlled by the frequency of landslide inducing storm events and recovery rate of the soil property between events despite such fluctuations there are gradual declines of 7 5 in average total carbon content of topsoil and 9 5 in average soil depth to bedrock since the time of forest clearance results have application to large scale sediment budget and water quality models and to the new zealand soil carbon monitoring system cms copyright 2012 john wiley sons ltd

combining spatial data in landslide reactivation susceptibility mapping a likelihood ratio based approach in w belgium a key issue in landslide susceptibility mapping concerns the relevance of the spatial data combination used in the prediction various combinations of high resolution predictor variables and possibilities of selecting them from a larger dataset are analysed the scarp reactivation of several landslides in a hilly region of w belgium is investigated at the pixel scale the susceptibility modelling uses the reactivated scarp segments as the dependent variable and 13 factors at a 2 m resolution related to topography hydrology land use and lithology as potential independent variables the modelling uses a likelihood ratio approach based on the comparison for each independent variable between two empirical distribution functions edfs respectively for the reactivated and non reactivated areas it uses these edfs as favourability values to build membership values and combine them with a fuzzy gamma operator five different data combinations are tested and compared by analysing the prediction rate curves obtained by cross validation the geomorphological value of the resulting susceptibility maps is also discussed this research shows relevant results for predicting the susceptibility to scarp reactivation we highlight the need for testing several data combinations and underline that combining quantitative criteria with expert opinion is an asset for reliable predictions

landslide susceptibility assessment what are the effects of mapping unit and mapping method 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

landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system along a road section in the northern himalayas india landslide studies are commonly guided by ground knowledge and field measurements of rock strength and slope failure criteria with increasing sophistication of gis based statistical methods however landslide susceptibility studies benefit from the integration of data collected from various sources and methods at different scales this study presents a logistic regression method for landslide susceptibility mapping and verifies the result by comparing it with the geotechnical based slope stability probability classification sspc methodology the study was carried out in a landslide prone national highway road section in the northern himalayas india logistic regression model performance was assessed by the receiver operator characteristics roc curve showing an area under the curve equal to 0 83 field validation of the sspc results showed a correspondence of 72 between the high and very high susceptibility classes with present landslide occurrences a spatial comparison of the two susceptibility maps revealed the significance of the geotechnical based sspc method as 90 of the area classified as high and very high susceptible zones by the logistic regression method corresponds to the high and very high class in the sspc method on the other hand only 34 of the area classified as high and very high by the sspc method falls in the high and very high classes of the logistic regression method the underestimation by the logistic regression method can be attributed to the generalisation made by the statistical methods so that a number of slopes existing in critical equilibrium condition might not be classified as high or very high susceptible zones

exploring the effect of absence selection on landslide susceptibility models a case study in sicily italy abstract a statistical approach was employed to model the spatial distribution of rainfall triggered landslides in two areas in sicily italy that occurred during the winter of 2004 2005 the investigated areas are located within the belice river basin and extend for 38 5 and 10 3 km 2 respectively a landslide inventory was established for both areas using two google earth images taken on october 25th 2004 and on march 18th 2005 to map slope failures activated or reactivated during this interval geographic information systems gis were used to prepare 5 m grids of the dependent variables absence presence of landslide and independent variables lithology and 13 dem derivatives multivariate adaptive regression splines mars were applied to model landslide susceptibility whereas receiver operating characteristic roc curves and the area under the roc curve auc were used to evaluate model performance to evaluate the robustness of the whole procedure we prepared 10 different samples of positive landslide presence and negative landslide absence cases for each area absences were selected through two different methods i extraction from randomly distributed circles with a diameter corresponding to the mean width of the landslide source areas and ii selection as randomly distributed individual grid cells a comparison was also made between the predictive performances of models including and not including the lithology parameter the models trained and tested on the same area demonstrated excellent to outstanding fit auc 0 8 on the other hand predictive skill decreases when measured outside the calibration area although most of the landslides occur where susceptibility is high and the overall model performance is acceptable auc 0 7 the results also showed that the accuracy of the landslide susceptibility models is higher when lithology is included in the statistical analysis models whose absences were selected using random circles showed a significantly better performance when learning and validation samples were extracted from the same area whereas conversely no significant difference was observed when testing the models outside the training area

combining landslide susceptibility maps obtained from frequency ratio logistic regression and artificial neural network models using aster images and gis 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

exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps abstract empirical models are frequently applied to produce landslide susceptibility maps for large areas subsequent quantitative validation results are routinely used as the primary criteria to infer the validity and applicability of the final maps or to select one of several models this study hypothesizes that such direct deductions can be misleading the main objective was to explore discrepancies between the predictive performance of a landslide susceptibility model and the geomorphic plausibility of subsequent landslide susceptibility maps while a particular emphasis was placed on the influence of incomplete landslide inventories on modelling and validation results the study was conducted within the flysch zone of lower austria 1 354 km 2 which is known to be highly susceptible to landslides of the slide type movement sixteen susceptibility models were generated by applying two statistical classifiers logistic regression and generalized additive model and two machine learning techniques random forest and support vector machine separately for two landslide inventories of differing completeness and two predictor sets the results were validated quantitatively by estimating the area under the receiver operating characteristic curve auroc with single holdout and spatial cross validation technique the heuristic evaluation of the geomorphic plausibility of the final results was supported by findings of an exploratory data analysis an estimation of odds ratios and an evaluation of the spatial structure of the final maps the results showed that maps generated by different inventories classifiers and predictors appeared differently while holdout validation revealed similar high predictive performances spatial cross validation proved useful to expose spatially varying inconsistencies of the modelling results while additionally providing evidence for slightly overfitted machine learning based models however the highest predictive performances were obtained for maps that explicitly expressed geomorphically implausible relationships indicating that the predictive performance of a model might be misleading in the case a predictor systematically relates to a spatially consistent bias of the inventory furthermore we observed that random forest based maps displayed spatial artifacts the most plausible susceptibility map of the study area showed smooth prediction surfaces while the underlying model revealed a high predictive capability and was generated with an accurate landslide inventory and predictors that did not directly describe a bias however none of the presented models was found to be completely unbiased this study showed that high predictive performances cannot be equated with a high plausibility and applicability of subsequent landslide susceptibility maps we suggest that greater emphasis should be placed on identifying confounding factors and biases in landslide inventories a joint discussion between modelers and decision makers of the spatial pattern of the final susceptibility maps in the field might increase their acceptance and applicability