Landslide susceptibility assessment in Apulian Southern Apennine: heuristic vs. statistical methods

In this paper, a bivariate-heuristic model (modified Stevenson’s method) and two multivariate statistical procedures (discriminant analysis and logistic regression) were used in order to assess and map landslide susceptibility in the north-western side of Daunia region (Apulia, Southern Italy). The whole Daunia region is characterized by complex and composite landslides, which are located on clayey slopes, near urban centers, affecting structures and infrastructures. The high predisposition to landsliding of the Daunia hillslopes is related to the very poor strength properties of clayey formations. The comparative analysis of landslide susceptibility using different methods, on the same test site and with the same inventory map allowed understanding the dependence of the results from the dataset and the capability of models under different levels of use, from expert to simple operator. By comparing the performance of the three models through the success rate curves, it emerges that the simple modified Stevenson’s method produces reliable outcomes, comparable with those deriving from more complex multivariate statistical models. This result is related to the characteristics of clayey slopes, in which the landslide occurrence is so much controlled by the poor strength properties of the clayey formations that the multivariate analysis of a large set of morphometric, geological and land-use variables results to be somehow superfluous. This suggests that, for clayey slopes, a simple, easy-to-manage bivariate-heuristic model based on expert opinion can be used with reliable results.     

Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania)

The Sibiciu Basin is located in Romania between the Buzău Mountains and the Buzau Subcarpathians (Curvature Carpathians and Subcarpathians). The geology of the basin consists of Paleogene flysch deposits represented by an alternation of sandstones, marls, clays and schists and Neogene deposits represented by marls, clays and sands. The area is affected by different types of landslides (shallow, medium-deep and deep-seated failures). In Romania, in the last decades, direct and indirect methods have been applied for landslide susceptibility assessment. The most utilized before 2000 were based on qualitative approaches. This study evaluates the landslide susceptibility in the Sibiciu Basin using a bivariate statistical analysis and an index of entropy. A landslide inventory map was prepared, and a susceptibility estimate was assessed based on the following parameters which influence the landslide occurrence: slope angle, slope aspect, curvature, lithology and land use. The landslide susceptibility map was divided into five classes showing very low to very high landslide susceptibility areas.     

Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances

The current research presents a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances. The study area covers the north of Tehran metropolitan, Iran. When conducting the study, in the first stage, a landslide inventory map with a total of 528 landslide locations was compiled from various sources such as aerial photographs, satellite images, and field surveys. Then, the landslide inventory was randomly split into a testing dataset 70 % (370 landslide locations) for training the models, and the remaining 30 % (158 landslides locations) was used for validation purpose. Twelve landslide conditioning factors such as slope degree, slope aspect, altitude, plan curvature, normalized difference vegetation index, land use, lithology, distance from rivers, distance from roads, distance from faults, stream power index, and slope-length were considered during the present study. Subsequently, landslide susceptibility maps were produced using binary logistic regression (BLR), analytical hierarchy process (AHP), and statistical index (SI) models in ArcGIS. The validation dataset, which was not used in the modeling process, was considered to validate the landslide susceptibility maps using the receiver operating characteristic curves and frequency ratio plot. The validation results showed that the area under the curve (AUC) for three mentioned models vary from 0.7570 to 0.8520 $ ({\text{AUC}}_{\text{AHP}} = 75.70\;\% ,\;{\text{AUC}}_{\text{SI}} = 80.37\;\% ,\;{\text{and}}\;{\text{AUC}}_{\text{BLR}} = 85.20\;\% ) $ ( AUC AHP = 75.70 % , AUC SI = 80.37 % , and AUC BLR = 85.20 % ) . Also, plot of the frequency ratio for the four landslide susceptibility classes of the three landslide susceptibility models was validated our results. Hence, it is concluded that the binary logistic regression model employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of study area. Meanwhile, the results obtained in this study also showed that the statistical index model can be used as a simple tool in the assessment of landslide susceptibility when a sufficient number of data are obtained.     

Landslide susceptibility assessment at the Wuning area,China: a comparison between multi-criteria decision making,bivariate statistical and machine learning methods

Natural Hazards - The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of...     

Landslide susceptibility assessment using object mapping units,decision tree,and support vector machine models in the Three Gorges of China

Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models.     

Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County,Three Gorges Reservoir,China

Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou County, China. First, a landslide inventory map was prepared using earlier geotechnical investigation reports, aerial images, and field surveys. Then, the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis. To determine the most effective causal factors, landslide susceptibility evaluations were performed based on four cases with different combinations of factors (“cases”). In the analysis, 465 (70%) landslide locations were randomly selected for model training, and 200 (30%) landslide locations were selected for verification. The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model. Finally, the receiver operating characteristic (ROC) curve was used to verify the accuracy of each model’s results for its respective optimal case. The ROC curve analysis showed that the machine learning model performed better than the other three models, and among the three statistical models, the IOE model with weight coefficients was superior.

     

Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco,Italy

This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen predisposing factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system. The used predictive models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and predisposing factors. Then, landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC?=?0.916) presented the most accurate map, followed by the ANFIS (AUC?=?0.889) and FR (AUC?=?0.888). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of predisposing factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

     

Comparative study of different machine learning models in landslide susceptibility assessment:A case study of Conghua District,Guangzhou,China

Machine learning is currently one of the research hotspots in the field of landslide prediction. To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District, which is the most prone to landslide disasters in Guangzhou, was selected for landslide susceptibility evaluation. The evaluation factors were selected by using correlation analysis and variance expansion factor method. Applying four machine learning methods namely ...     

基于快速聚类-信息量模型的汶川及周边两县滑坡易发性评价

滑坡灾害易发性评价研究对规划灾害区域、制定防灾策略等方面具有十分重要的意义。以滑坡灾害频发的汶川及周边两县（理县和茂县）为例,提出滑坡灾害易发性评价的快速聚类-信息量模型。选取坡度、高程、坡向、距构造的距离、距水系的距离、地层岩性和土地利用情况为对滑坡有重要影响的7个影响因子,并在二级因子的分类上,对上述前5个影响因子依据159处滑坡样本分别开展快速聚类分析,同时也给出了传统的等距分类法,以便与快速聚类方法形成对比,对后2个影响因子则以定性方法分类。根据上述二级分类方法的不同,以及滑坡样本是否考虑面积因素,将信息量模型细分为四类（模型a:快速聚类-数量模型、模型b:等距分类-数量模型、模型c:快速聚类-面积模型、模型d:等距分类-面积模型）,分别计算各二级指标信息量,并通过ArcGIS空间叠加分析得到研究区域信息量分布,然后通过自然断点法将研究区滑坡易发性划分为五个等级。以易发性递增原则和线下面积（Area Under Curve,AUC）作为精度评价指标,结果表明:①快速聚类模型（模型a和模型c）整体效果优于等距分类模型（模型b和模型d）;②相同分类方法下,面积模型（模型c与模型d）整体优于数量模型（模型a和模型b）;③在上述两项优势的加持下,模型c相较于模型b,评价精度明显提升,其AUC值从80.46%提高到87.25%。     

A new region-based preparatory factor for landslide susceptibility models: the total flux

This paper presents a new region-based preparatory factor, total flux (TF), for landslide susceptibility models (LSMs). TF takes into account the topography and hydrology conditions upstream of each gridded data cell and represents the total flux of water in the stream. The results show that TF is strongly associated with the occurrence of landslides and is a good preparatory factor for LSM. Using TF instead of a drainage distance factor in I-Lan region in Taiwan shows an improvement in the accuracy of the cumulative percentage of landslide occurrence of 44 and 14 % for the top 1 and 10 % susceptible areas, respectively. This significant improvement in accuracy in these high-risk areas is critical for preventing and mitigating the economic and human losses due to landslides.     

GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran,Iran)

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.     

Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost,Turkey) using conditional probability approach in GIS

On 19 February 2007, a landslide occurred on the Alaard?ç Slope, located 1.6 km south of the town of Yaka (Gelendost, Turkey.) Subsequently, the displaced materials transformed into a mud flow in E?lence Creek and continued 750 m downstream towards the town of Yaka. The mass poised for motion in the Yaka Landslide source area and its vicinity, which would be triggered to a kinetic state by trigger factors such as heavy or sustained rainfall and/or snowmelt, poise a danger in the form of loss of life and property to Yaka with its population of 3,000. This study was undertaken to construct a susceptibility mapping of the vicinity of the Yaka Landslide’s source area and to relate it to movement of the landslide mass with the goal of prevention or mitigation of loss of life and property. The landslide susceptibility map was formulated by designating the relationship of the effecting factors that cause landslides such as lithology, gradient, slope aspect, elevation, topographical moisture index, and stream power index to the landslide map, as determined by analysis of the terrain, through the implementation of the conditional probability method. It was determined that the surface area of the Goksogut formation, which has attained lithological characteristics of clayey limestone with a broken and separated base and where area landslides occur, possesses an elevation of 1,100–1,300 m, a slope gradient of 15 °–35 ° and a slope aspect between 0 °–67.5 ° and 157 °–247 °. Loss of life and property may be avoided by the construction of structures to check the debris mass in E?lence Creek, the cleaning of the canal which passes through Yaka, the broadening of the canal’s base area, elevating the protective edges along the canal and the establishment of a protective zone at least 10-m wide on each side of the canal to deter against damage from probable landslide occurrence and mud flow.     

Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models

This study applied, tested and compared a probability model, a frequency ratio and statistical model, a logistic regression to Damre Romel area, Cambodia, using a geographic information system. For landslide susceptibility mapping, landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and a spatial database was constructed from topographic maps, geology and land cover. The factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from lineament were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite imagery. The relationship between the factors and the landslides was calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient were overlaid to make landslide susceptibility map. Then the landslide susceptibility map was compared with known landslide locations and tested. As the result, the frequency ratio model (86.97%) and the logistic regression (86.37%) had high and similar prediction accuracy. The landslide susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.     

Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya

This study considers landslide susceptibility mapping by means of frequency ratio and artificial neural network approaches using geographic information system (GIS) techniques as a basic analysis tool. The selected study area was that of the Panchthar district, Nepal. GIS was used for the management and manipulation of spatial data. Landslide locations were identified from field survey and aerial photographic interpretation was used for location of lineaments. Ten factors in total are related to the occurrence of landslides. Based on the same set of factors, landslide susceptibility maps were produced from frequency ratio and neural network models, and were then compared and evaluated. The weights of each factor were determined using the back-propagation training method. Landslide susceptibility maps were produced from frequency ratio and neural network models, and they were then compared by means of their checking. The landslide location data were used for checking the results with the landslide susceptibility maps. The accuracy of the landslide susceptibility maps produced by the frequency ratio and neural networks is 82.21 and 78.25%, respectively.