Impact of seismic factors on landslide susceptibility zonation: a case study in part of Indian Himalayas

Landslides are one of the most widespread natural hazards in high mountain terrains such as the Himalayas, which are one of the youngest tectonically and seismically active mountain ranges in the world. The crustal movements along the longitudinal thrusts and transverse faults give rise to earthquakes and in turn initiate landslides in the region. In fact, in addition to various static factors causing landslides, earthquakes are one of the major causes of landslides. It is thus imperative to incorporate seismic factor also while carrying out landslide susceptibility zonation map preparation in a seismically active areas like Garhwal Himalayas. In this paper, a study on the effect of earthquakes on landslide susceptibility zonation has been demonstrated by taking Chamoli earthquake as an example.     

An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas

Landslide susceptibility zonation (LSZ) is necessary for disaster management and planning development activities in mountainous regions. A number of methods, viz. landslide distribution, qualitative, statistical and distribution-free analyses have been used for the LSZ studies and they are again briefly reviewed here. In this work, two methods, the Information Value (InfoVal) and the Landslide Nominal Susceptibility Factor (LNSF) methods that are based on bivariate statistical analysis have been applied for LSZ mapping in a part of the Himalayas. Relevant thematic maps representing various factors (e.g., slope, aspect, relative relief, lithology, buffer zones along thrusts, faults and lineaments, drainage density and landcover) that are related to landslide activity, have been generated using remote sensing and GIS techniques. The LSZ derived from the LNSF method, has been compared with that produced from the InfoVal method and the result shows a more realistic LSZ map from the LNSF method which appears to conform to the heterogeneity of the terrain.     

Relative effect-based landslide hazard zonation mapping in parts of Nilgiris, Tamil Nadu, South India

The Nilgiri massif, South India, is chronically prone to landslides due to deforestation and the resultant direct entry of rainwater and the final increases of pore pressure leading to landslides in the region. In order to understand such landslide causes, the relative effect method, a new technique, has been adopted for the study area. Among various methods, this is a statistical method developed within the framework of the Geographic Information System to map landslide hazard zones in a mountainous environment. To determine the relative effect (RE) of the factors influencing landslides, data layers of geology, land use/land cover, geomorphology, slope, lineament density, drainage density, and soil were analyzed by calculating the ratio of the unit portion in coverage and landslide, this function that is logarithmic. To quantify the magnitude of factors influencing each grid unit, REs were summed and classified into zones of low-, moderate-, and high-landslide hazard zones. It is also appropriate to follow suitable measures to prevent the landslides in the study area by involving all stockholders and with the active participation of local communities.     

Ensemble-based landslide susceptibility maps in Jinbu area, Korea

Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model.     

Statistical analysis of landslide susceptibility at Yongin, Korea

The aim of this study is to evaluate the susceptibility of landslides at Yongin, Korea, using a geographic information system (GIS). Landslide locations were identified in the Yongin area from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, timber cover, and geology. These data were collected and constructed into a spatial database using GIS. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, age, diameter, and density of timber were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM satellite image. Landslide susceptibility was analyzed using the landslide occurrence factors by probability and logistic regression methods. The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy. The results can be used to reduce associated hazards, and to plan land use and construction.     

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.     

Neural networks and landslide susceptibility: a case study of the urban area of Potenza

For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful information regarding possible future phenomena. Such maps represent a fundamental step in the drawing up of adequate measures of landslide hazard mitigation. However, for a complete estimation of landslide hazard, meant as the degree of probability that a landslide occurs in a given area, within a given space of time, detailed and uniformly distributed data regarding their incidence and causes are required. This information, while obtainable through laborious historical research, is usually partial, incomplete and uneven, and hence, unsatisfactory for zoning on a regional scale. In order to carry this out effectively, the utilization of spatial estimation of the relative levels of landslide hazard in the various areas was considered opportune. These areas were classified according to their levels of proneness to landslide activity without taking recurrence periods into account. Various techniques were developed in order to obtain upheaval numerical estimates. The method used in this study, which was applied in the area of Potenza, is based on techniques derived from artificial intelligence (Artificial Neural Network—ANN). This method requires the definition of appropriate thematic layers, which parameterize the area under study. These are recognized by means of specific analyses in a functional relationship to the event itself. The parameters adopted are: slope gradient, slope aspect, topographical index, topographical shape, elevation, land use and lithology.     

A simple method to help determine landslide susceptibility from spaceborne InSAR data: the Montescaglioso case study

On December 3, 2013, a large complex landslide was triggered SW of the town of Montescaglioso (Southern Italy), causing the destruction of roads, commercial buildings and private dwellings, as well as several direct and indirect economic losses. A set of interferometric ground measurements acquired by the Cosmo-SkyMed satellite constellation and processed by means of the SqueeSAR algorithm was used to study the pre-event slope displacements in the entire Montescaglioso municipal area. Data span from January 30, 2012, to December 2, 2013, and show average line-of-sight velocities of 1–10 mm/year in the slope sector ultimately affected by the collapse. In retrospect, a time series analysis of the radar targets was performed in order to identify and characterize all the slope instabilities in proximity of the town. This was based on the setup of characteristic thresholds of displacement. The procedure permitted to locate several areas which recurrently exceeded these previously established thresholds, in consistency with the amount of precipitation. In particular, the major source of potential hazard in the area was indeed found where the December 3, 2013, landslide eventually occurred. The results of this quick data processing technique were validated through comparison with two independently developed landslide maps. This simple method, which is not supposed to diminish the importance of geomorphologic field surveys, could improve both the accuracy and the update rate of landslide susceptibility maps. Not relying on arbitrary or empirically derived approaches, it has the advantage of computing statistically based thresholds specific for each time series. By indicating the slope sectors in higher need of deeper in situ investigation, more support could be provided to administrative bodies for the processes of risk assessment and management.     

Inclusion of earthquake strong ground motion in a geographic information system-based landslide susceptibility zonation in Garhwal Himalayas

Garhwal Himalayas are seismically very active and simultaneously suffering from landslide hazards. Landslides are one of the most frequent natural hazards in Himalayas causing damages worth more than one billion US$ and around 200 deaths every year. Thus, it is of paramount importance to identify the landslide causative factors to study them carefully and rank them as per their influence on the occurrence of landslides. The difference image of GIS-derived landslide susceptibility zonation maps prepared for pre- and post-Chamoli earthquake shows the effect of seismic shaking on the occurrence of landslides in the Garhwal Himalaya. An attempt has been made to incorporate seismic shaking parameters in terms of peak ground acceleration with other static landslide causative factors to produce landslide susceptibility zonation map in geographic information system environment. In this paper, probabilistic seismic hazard analysis has been carried out to calculate peak ground acceleration values at different time periods for estimating seismic shaking conditions in the study area. Further, these values are used as one of the causative factors of landslides in the study area and it is observed that it refines the preparation of landslide susceptibility zonation map in seismically active areas like Garhwal Himalayas.     

Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy

In this article, the results of a study aimed to assess the landslide susceptibility in the Calaggio Torrent basin (Campanian Apennines, southern Italy) are presented. The landslide susceptibility has been assessed using two bivariate-statistics-based methods in a GIS environment. In the first method, widely used in the existing literature, weighting values (Wi) have been calculated for each class of the selected causal factors (lithology, land-use, slope angle and aspect) taking into account the landslide density (detachment zones + landslide body) within each class. In the second method, which is a modification of the first method, only the landslide detachment zone (LDZ) density has been taken into account to calculate the weighting values. This latter method is probably characterized by a major geomorphological coherence. In fact, differently from the landslide bodies, LDZ must necessarily occur in geoenvironmental classes prone to failure. Thus, the calculated Wi seem to be more reliable in estimating the propensity of a given class to generate failure. The thematic maps have been reclassified on the basis of the calculated Wi and then overlaid, with the purpose to produce landslide susceptibility maps. The used methods converge both in indicating that most part of the study area is characterized by a high–very high landslide susceptibility and in the location and extent of the low-susceptible areas. However, an increase of both the high–very high and moderate–high susceptible areas occurs in using the second method. Both the produced susceptibility maps have been compared with the geomorphological map, highlighting an excellent coherence which is higher using method-2. In both methods, the percentage of each susceptibility class affected by landslides increases with the degree of susceptibility, as expected. However, the percentage at issue in the lowest susceptibility class obtained using method-2, even if low, is higher than that obtained using method-1. This suggests that method-2, notwithstanding its major geomorphological coherence, probably still needs further refinements.     

Mapping landslide susceptibility with frequency ratio,statistical index,and weights of evidence models: a case study in northern Iran

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.     

Geo-diversity and its hydrological response in relation to landslide susceptibility in the Himalaya: a GIS-based case study

Assessment and inventory of landslide susceptibility are essential for the formulation of successful disaster mitigation plans. The objective of this study was to assess landslide susceptibility in relation to geo-diversity and its hydrological response in the Lesser Himalaya with a case study using Geographic Information System (GIS) technology. The Dabka watershed, which constitutes a part of the Kosi Basin in the Lesser Himalaya, India, in the district of Nainital, has been selected for the case illustration. The study constitutes three GIS modules: geo-diversity informatics, hydro informatics and landslide informatics. Through the integration and superimposing of spatial data and attribute data of all three GIS modules, Landslide Susceptibility Index (LSI) has been prepared to identify the level of susceptibility for landslide hazards. This resonance study, carried out over a period of five years (2007–2011), found that areas of most stressed geo-diversity (comprising very steep slopes above 30°, geology of Lower Krol and Lariakanta formation, geomorphology of moist areas and debris sites, land use of barren land with a very high drainage frequency and spring density) have a high landslide susceptibility because of high rate of average runoff (33 l/s/km²), flood magnitude (307.28 l/s/km²), erosion (398 tons/km²) and landslide density (5–10 landslides/km²). The areas of least stressed geo-diversity (comprising gentle slopes below 10°, geology of Kailakhan and Siwalik formation, geomorphology of depositional terraces, land use of dense forest with low drainage frequency and spring density) have the lowest landslide susceptibility because of the low rate of average runoff (6.27 l/s/km²), flood magnitude (20.49 l/s/km²), erosion (65.80 tons/km²) and landslide density (1–2 landslides/km²).     

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 ...     

海底隧道风化花岗岩流变试验研究

采用全自动三轴流变试验机对厦门东通道海底隧道风化槽地段岩石进行三轴压缩流变试验,研究了岩石在不同围压和不同应力水平作用下轴向应变随时间的变化规律。试验表明,强风化花岗岩时效特性较全风化花岗岩更加明显;围压对岩石蠕变变形存在很大影响,围压越大,蠕变变形量越小。通过分析岩石压缩流变过程中应力-应变关系可知,蠕变变形在弹性阶段不对岩石整体构成明显损伤。而进入塑性阶段后,黏塑性变形对岩石破坏影响较大。建议通过支护增加围压,以提高隧道围岩屈服强度和减小流变变形,防止隧道失稳破坏。     

黄土滑坡滑带土的特点——以天水椒树湾滑坡为例

黄土滑坡已成为影响黄土地区公路、铁路等基础设施建设的主要因素,滑坡的形成在一定程度上就是滑带土变形破坏过程。从黄土滑坡滑带土的分类、物理力学性质、宏观与微观特征等方面进行了较系统的研究,探究其变形和破坏规律。结合甘肃天水的黄土滑坡实例进行了具体分析。     

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.     

Characterizing groundwater recharge using oxygen and hydrogen isotopes: a case study in a temperate forested region,South Korea

In the cool temperate region of South Korea, oxygen and hydrogen isotopes of groundwater, lake water, and precipitation were studied to determine the season of groundwater recharge. All the groundwater samples, irrespective of season, on δ¹⁸O–δ²H scale plotted along the summer precipitation, suggesting summer precipitation largely modulates recharge. The deuterium excess values of groundwater (d-excess) show clear seasonal difference, higher in winter (> 18‰) and lower in summer (< 10‰). And its resemblance to the summer precipitation d-excess value further suggests dominant role of summer precipitation in groundwater recharge. Based on the mass balance equation, with end-member d-excess values of seasonal precipitation and groundwater as input variables, groundwater is composed of 66% summer and 34% winter precipitation. Despite the study area being heavily forested, summer rainfall contribution higher than winter suggests that evapotranspiration effect is minimal in the region; may be due to thin sand–gravel-based porous soil overlying highly weathered granitic rock system.