Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy)

Statistical and deterministic methods are widely used in geographic information system based landslide susceptibility mapping. This paper compares the predictive capability of three different models, namely the Weight of Evidence, the Fuzzy Logic and SHALSTAB, for producing shallow earth slide susceptibility maps, to be included as informative layers in land use planning at a local level. The test site is an area of about 450 km² in the northern Apennines of Italy where, in April 2004, rainfall combined with snowmelt triggered hundreds of shallow earth slides that damaged roads and other infrastructure. An inventory of the landslides triggered by the event was obtained from interpretation of aerial photos dating back to May 2004. The pre-existence of mapped landslides was then checked using earlier aerial photo coverage. All the predictive models were run on the same set of geo-environmental causal factors: soil type, soil thickness, land cover, possibility of deep drainage through the bedrock, slope angle, and upslope contributing area. Model performance was assessed using a threshold-independent approach (the ROC plot). Results show that global accuracy is as high as 0.77 for both statistical models, while it is only 0.56 for SHALSTAB. Besides the limited quality of input data over large areas, the relatively poorer performance of the deterministic model maybe also due to the simplified assumptions behind the hydrological component (steady-state slope parallel flow), which can be considered unsuitable for describing the hydrologic behavior of clay slopes, that are widespread in the study area.     

The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling

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.     

The chaotic characteristics of landslide evolution: a case study of Xintan landslide

A new method based on the chaos theory is used to assess the evolution process of a slope system. The method is applied to the Xintan landslide and the results show: (1) the slope movement is a complex process of the slope going in and out of the stable and chaotic state; (2) the method reveals the evolution process of the slope pointing to the slope failure while the observed movement shows a simple monotonic increase with time; (3) the method is not sufficiently mature to precisely predict the time of failure but it has potential for improvement with further research and more field data for analysis.     

Rapid terrain-based mapping of some volcaniclastic flow hazard using Gis-based automated methods: a case study from southern Campania,Italy

Destructive volcaniclastic flows are among the most recurrent and dangerous natural phenomena in volcanic areas. They can originate not only during or shortly after an eruption (syn-eruptive) but also during a period of volcanic quiescence (inter-eruptive), when heavy and/or persistent rains remobilize loose pyroclastic deposits. The area in Italy most prone to such flows is that of the Apennine Mountains bordering the southern Campania Plain. These steep slopes are covered by pyroclastic material of variable thickness (a few cm to several m) derived from the explosive activity of the Somma-Vesuvius and Campi Flegrei volcanoes a few tens of kilometers to the west. The largest and most recent devastating event occurred on May 5, 1998, causing the death of more than 150 people and considerable damage to villages at the foot of the Apennine Mountains. This tragic event was only the most recent of a number of volcaniclastic flows affecting the area in both historical and prehistoric times. Historical accounts report that more than 500 events have occurred in the last five centuries and that more than half of these occurred in the last 100 years, causing hundreds of deaths. In order to improve volcaniclastic flow hazard zonation and risk mitigation in the study area, we produced a zonation map that identifies the drainage basins potentially prone to disruption. This map was obtained by combining morphological characteristics (concavity and basin shape factor) and the mean slope distribution of drainage basins derived from a digital elevation model with a 10-m resolution. These parameters allowed for the classification of 1,069 drainage basins, which have been grouped into four different classes of proneness to disruption: low, moderate, high and very high. The map compiled in a GIS environment, as well as the linked database, can be rapidly queried.     

PFR model and GiT for landslide susceptibility mapping: a case study from Central Alborz, Iran

In northern parts of Iran such as the Alborz Mountain belt, frequent landslides occur due to a combination of climate and geologic conditions with high tectonic activities. This results in millions of dollars of financial damages annually excluding casualties and unrecoverable resources. This paper evaluates the landslide susceptible areas in Central Alborz using the probabilistic frequency ratio (PFR) model and Geo-information Technology (GiT). The landslide location map in this study has been generated based on image elements interpreted from IRS satellite data and field observations. The display, manipulation and analysis have been carried out to evaluate layers such as geology, geomorphology, soil, slope, aspect, land use, distance from faults, lineaments, roads and drainages. The validation group of actual landslides and relative operation curve method has been used to increase the accuracy of the final landslide susceptibility map. The area under the curve evaluates how well the method predicts landslides. The results showed a satisfactory agreement of 91% between prepared susceptibility map and existing data on landslide locations.     

A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey)

This study aimed to investigate the parameter effects in preparing landslide susceptibility maps with a data-driven approach and to adapt this approach to analytical hierarchy process (AHP). For this purpose, at the first stage, landslide inventory of an area located in the Western Black Sea region of Turkey covering approximately 567?km² was prepared, and a total of 101 landslides were mapped. In order to assess the landslide susceptibility, a total of 13 parameters were considered as the input parameters: slope, aspect, plan curvature, topographical elevation, vegetation cover index, land use, distance to drainage, distance to roads, distance to structural elements, distance to ridges, stream power index, sediment transport capacity index, and wetness index. AHP was selected as the major assessment methodology since the adapted approach and AHP work in data pairs. Adapted to AHP, a similarity relation?Cbased approach, namely landslide relation indicator (LRI) for parameter selection method, was also proposed. AHP and parametric effect analyses were performed by the proposed approach, and seven landslide susceptibility maps were produced. Among these maps, the best performance was gathered from the landslide susceptibility map produced by 9 parameter combinations using area under curve (AUC) approach. For this map, the AUC value was calculated as 0.797, while the others ranged between 0.686 and 0.771. According to this map, 38.3?% of the study area was classified as having very low, 8.5?% as low, 15.0?% as moderate, 20.3?% as high, and 17.9?% as very high landslide susceptibility, respectively. Based on the overall assessments, the proposed approach in this study was concluded as objective and applicable and yielded reasonable results.     

Coupling fluvial processes and landslide distribution toward geomorphological hazard assessment: a case study in a transient landscape in Japan

This study quantified the relationship among deep-seated gravitational slope deformations (DGSDs), landslides, and river rejuvenation in the upper reaches of the Kumano River in the Kii Mountains of Japan, an area of frequent bedrock landslides. River profiles and hillslope landforms were examined, and high-resolution digital elevation models (DEMs) were used to identify DGSDs and landslides. Many of the deep-seated landslides were associated with rainstorms in 1889 and 2011. Landslide volumes were related to landslide areas on the basis of 52 deep-seated landslides that failed during the 2011 rainfall, providing basic data for landscape denudation and sediment yield. River rejuvenation occurred stepwise, incising moderate relief paleosurfaces and forming two series of knickpoints and V-shaped inner gorges that are up to 400-m deep. More than 65% of DGSDs and 75% of the landslides were located in association with the incised inner gorges along the peripheries of the paleosurfaces or were entirely contained within the inner gorges. DGSDs and landslides associated with the incised inner valley slopes tended to be larger than those developed within the paleosurfaces and may be long-term transient hillslope responses to river incision. Hillslope undercutting caused by rejuvenated river incision may play an important role in long-term slope stability and distribution of mass movements, and could serve as an indicator of landslide hazard.     

不同采样点数量下土壤有机质含量空间预测方法对比

运用普通克里格、泛克里格、协同克里格和回归克里格4种方法,结合由DEM获取的高程因子以及土壤全氮和阳离子交换量（CEC）,预测了黑龙江省海伦市耕地有机质含量的空间分布。不同样点数量下海伦市土壤有机质含量的空间变异结构分析表明,样点数量多并不一定能够识别土壤有机质含量的结构性连续组分,最优化的布置采样点位置可能比单纯增加...     

基于GIS的滑坡空间数据库研究--以云南小江流域为例

滑坡作为一种典型的地质灾害现象,有其特有的属性特征、方法及事件特征。在面向对象的GIS中,滑坡因素空间数据库是描述滑坡对象的主要指标系统。滑坡因素空间数据库包括滑坡因子数据库和滑坡分布数据库。滑坡因子主要分为静力因子和动力因子。静力因子决定了滑坡空间分布特征及规律。动力因子与新滑坡的变形失稳及老滑坡的再次活动密切相关。通过航片解译和现场勘查等工作建立的滑坡因素空间分布数据库是建立滑坡GIS分析模型的重要环节。滑坡因子数据库与空间分布数据库不仅包括属性数据库,而且包括图形数据库。它们的建立需要经过概念模式设计,逻辑设计,物理设计3个重要的步骤。云南小江流域是我国西南地区典型的滑坡等地质灾害多发区,以其为例对滑坡空间数据库的设计与创建过程进行了详细的说明。     

Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway)

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.     

A seismic landslide hazard analysis with topographic effect,a case study in the 99 Peaks region,Central Taiwan

It has been known that ground motion amplitude will be amplified at mountaintops; however, such topographic effects are not included in conventional landslide hazard models. In this study, a modified procedure that considers the topographic effects is proposed to analyze the seismic landslide hazard. The topographic effect is estimated by back analysis. First, a 3D dynamic numerical model with irregular topography is constructed. The theoretical topographic amplification factors are derived from the dynamic numerical model. The ground motion record is regarded as the reference motion in the plane area. By combining the topographic amplification factors with the reference motions, the amplified acceleration time history and amplified seismic intensity parameters are obtained. Newmark’s displacement model is chosen to perform the seismic landslide hazard analysis. By combining the regression equation and the seismic parameter of peak ground acceleration and Arias intensity, the Newmark’s displacement distribution is generated. Subsequently, the calculated Newmark’s displacement maps are transformed to the hazard maps. The landslide hazard maps of the 99 Peaks region, Central Taiwan are evaluated. The actual landslide inventory maps triggered by the 21 September 1999, Chi-Chi earthquake are compared with the calculated hazard maps. Relative to the conventional procedure, the results show that the proposed procedures, which include the topographic effect can obtain a better result for seismic landslide hazard analysis. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Environmental hazard analysis of a gypsum karst depression area with geophysical methods: a case study in Sivas (Turkey)

Because the mixture of seawater and freshwater in the Gyeongin-Ara Waterway in South Korea can lead to the intrusion of saline water into surrounding aquifers, systematic management through the establishment of a groundwater protection area is required. The analytic hierarchy process (AHP) model is used to delineate this protection area based on two primary factors and five secondary factors related to saline water intrusion. The study area is divided into 987 gridded cells with a unit size of 100 × 100 m, and the final evaluation score for each cell is calculated using the AHP model. Consequently, several artificial neural network models based on a multilayer perceptron are developed using the AHP’s secondary criteria and the evaluation score. Comparing the evaluation scores of ANN and AHP, more than 180 samples are required in the ANN model to insure high R2 between the original and estimated values. The ANN model is more consistent than the AHP model when determining groundwater protection area, because it can be re-constructed due to the changes in some secondary criteria and also changed due to a standardization process. The final evaluation score by the ANN model based on 300 samples, with the highest R2, is calculated and the regions with a score higher than 2.0 are selected as the groundwater protection area, accounting for 15% of the total cells. This area is similar to the range within approximately 200 m of the GA Waterway and also includes some changing sites in hydrogeochemistry and electric conductivity, which is produced by saline water intrusion. If the land-use type, groundwater levels, and some other criteria change at any cell, the ANN model can be re-executed to verify whether the cell belongs to a groundwater protection area. Considering that salinity of groundwater near the waterway can be affected by various factors including well depth, pumping conditions, and groundwater levels, the ANN model, which is a non-linear model, can be more effective for prediction than the AHP model.     

Evaluation of the consistency of landslide susceptibility mapping: a case study from the Kankai watershed in east Nepal

GIS-based landslide susceptibility maps for the Kankai watershed in east Nepal are developed using the frequency ratio method and the multiple linear regression technique. The maps are derived from comparing observed landslides with possible causative factors: slope angle, slope aspect, slope curvature, relative relief, distance from drainage, land use, geology, distance from faults and mean annual rainfall. The consistency of the maps is evaluated using landslide density analysis, success rate analysis and spatially agreed area approach. The first two analyses produce almost identical quantitative results, whereas the last approach is able to reveal spatial differences between the maps and also to improve predictions in the agreed high landslide-susceptible area.