Application and verification of a fractal approach to landslide susceptibility mapping

Landslide susceptibility mapping is essential for land-use activities and management decision making in hilly or mountainous regions. The existing approaches to landslide susceptibility zoning and mapping require many different types of data. In this study, we propose a fractal method to map landslide susceptibility using historical landslide inventories only. The spatial distribution of landslides is generally not uniform, but instead clustered at many different scales. In the method, we measure the degree of spatial clustering of existing landslides in a region using a box-counting method and apply the derived fractal clustering relation to produce a landslide susceptibility map by means of GIS-supported spatial analysis. The method is illustrated by two examples at different regional scales using the landslides inventory data from Zhejiang Province, China, where the landslides are mainly triggered by rainfall. In the illustrative examples, the landslides from the inventory are divided into two time periods: The landslides in the first period are used to produce a landslide susceptibility map, and those in the late period are taken as validation samples for examining the predictive capability of the landslide susceptibility maps. These examples demonstrate that the landslide susceptibility map created by the proposed technique is reliable.     

Landslide Susceptibility Zoning North of Yenice (NW Turkey) by Multivariate Statistical Techniques

Dramatic effects resulting from landslides on human life and economy of many nations are observed sometimes throughout the world. Landslide inventory and susceptibility mapping studies are accepted as the first stage of landslide hazard mitigation efforts. Generally, these landslide inventory studies include identification and location of landslides. The main benefit is to provide a basis for statistical susceptibility zoning studies. In the present study, a landslide susceptibility zoning near Yenice (NW Turkey) is carried out using the factor analysis approach. The study area is approximately 64 km² and 57 landslides were identified in this area. The area is covered completely by Ulus Formation that has a flysh-like character. Slope angle, elevation, slope aspect, land-use, weathering depth and water conditions were considered as the main conditioning factors while the heavy precipitation is the main trigger for landsliding. According to the results of factor analysis, the importance weights for slope angle, land-use, elevation, dip direction, water conditions and weathering depth were determined as 45.2%, 22.4%, 12.5%, 8.8%, 8.1% and 3.0% respectively. Also, using these weights and the membership values of each conditioning factor, the membership value for landslide susceptibility was introduced. In the study area, the lowest membership value for landslide susceptibility was calculated as 0.20. Consequently, combining all results, a landslide susceptibility map was obtained. Compared with the obtained map, a great majority of the landslides (86 %) identified in the field were found to be located in susceptible and highly susceptible zones.     

A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at ?zmir, Turkey

The main purpose of this study is to compare the use of logistic regression, multi-criteria decision analysis, and a likelihood ratio model to map landslide susceptibility in and around the city of İzmir in western Turkey. Parameters, such as lithology, slope gradient, slope aspect, faults, drainage lines, and roads, were considered. Landslide susceptibility maps were produced using each of the three methods and then compared and validated. Before the modeling and validation, the observed landslides were separated into two groups. The first group was for training, and the other group was for validation steps. The accuracy of models was measured by fitting them to a validation set of observed landslides. For validation process, the area under curvature (AUC) approach was applied. According to the AUC values of 0.810, 0.764, and 0.710 for logistic regression, likelihood ratio, and multi-criteria decision analysis, respectively, logistic regression was determined to be the most accurate method among the other used landslide susceptibility mapping methods. Based on these results, logistic regression and likelihood ratio models can be used to mitigate hazards related to landslides and to aid in land-use planning.     

Landslide susceptibility and hazard mapping in Australia for land-use planning — with reference to challenges in metropolitan suburbia

This paper provides an overview of the history and current status of landslide susceptibility and hazard mapping for land-use zoning in Australia. It also describes a case study of landslide hazard mapping in a medium density, coastal, suburban residential area of metropolitan Sydney, New South Wales, Australia, with relatively steep terrain. Issues covered include identification and mapping of existing and potential landslides, and susceptibility and hazard zoning for regulatory management and land-use planning. The method involves application of the principles contained within the AGS (2000) guideline, and as updated by the AGS (2007 a,b,c,d,e) suite of guidelines.     

Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study

The crucial and difficult task in landslide susceptibility analysis is estimating the probability of occurrence of future landslides in a study area under a specific set of geomorphic and topographic conditions. This task is addressed with a data-driven probabilistic model using likelihood ratio or frequency ratio and is applied to assess the occurrence of landslides in the Tevankarai Ar sub-watershed, Kodaikkanal, South India. The landslides in the study area are triggered by heavy rainfall. Landslide-related factors—relief, slope, aspect, plan curvature, profile curvature, land use, soil, and topographic wetness index proximity to roads and proximity to lineaments—are considered for the study. A geospatial database of the related landslide factors is constructed using Arcmap in GIS environment. Landslide inventory of the area is produced by detailed field investigation and analysis of the topographical maps. The results are validated using temporal data of known landslide locations. The area under the curve shows that the accuracy of the model is 85.83%. In the reclassified final landslide susceptibility map, 14.48% of the area is critical in nature, falling under the very high hazard zone, and 67.86% of the total validation dataset landslides fall in this zone. This landslide susceptibility map is a vital tool for town planning, land use, and land cover planning and to reduce risks caused by landslides.     

Landslide susceptibility assessment of South Pars Special Zone,southwest Iran

This study assesses the landslide susceptibility of the South Pars Special Zone (SPSZ) region that is located in southwest Iran. For this purpose, a combinatorial method containing multi-criteria decision-making, likelihood ratio and fuzzy logic was applied in two levels (regional and local) at three critical zones (northwest, middle and southeast of the project area). The analysis parameters were categorised in seven main triggering factors such as climatology, geomorphology, geology, geo-structure, seismic activity, landslide prone areas and man-made activities which have different classes with multi-agent partnership correlations. Landslide susceptibility maps were prepared for these levels and zones after purified and enriched fuzzy trending runs were performed. According to the results of the risk-ability assessment of the landslide occurrences for SPSZ, the north part of the study area which includes the south edge of the Assalouyeh anticline and the southern part of the Kangan anticline were estimated as high-risk potential areas that were used in landslide hazard mitigation assessment and in land-use planning.     

Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region,Tehran, Iran

Landslide susceptibility mapping is among the useful tools applied in disaster management and planning development activities in mountainous areas. The susceptibility maps prepared in this research provide valuable information for landslide hazard management in Lashgarak region of Tehran. This study was conducted to, first, prepare landslide susceptibility maps for Lashgarak region and evaluate landslide effect on mainlines and, second, to analyze the main factors affecting landslide hazard increase in the study area in order to propose efficient strategies for landslide hazard mitigation. A GIS-based multi-criteria decision analysis model (fuzzy logic) is used in the present work for scientific evaluation of landslide susceptible areas in Lashgarak region. To this end, ArcGIS, PCIGeomatica, and IDIRISI software packages were used. Eight information layers were selected for information analysis: ground strength class, slope angle, terrain roughness, normalized difference moisture index, normalized difference vegetation index, distance from fault, distance from the river, and distance from the road. Next, eight different scenarios were created to determine landslide susceptibility of the study area using different operators (intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT), and different fuzzy gamma values) of fuzzy overlay approach. After that, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared. The results revealed the excellent consistency of landslide susceptibility map prepared using the fuzzy union (OR) operator with landslide distribution map in the study area. Eventually, the accuracy of landslide susceptibility map prepared using the fuzzy union (OR) operator was evaluated using the frequency ratio diagram. The results showed that frequency values of the landslides gradually increase from “low susceptibility” to high “susceptibility” as 88.34% of the landslides are categorized into two “high” and “very high” susceptibility classes, implying the satisfactory consistency between the landslide susceptibility map prepared using fuzzy union (OR) operator and landslide distribution map.     

Landslide hazard zonation using analytical hierarchy process along National Highway-3 in mid Himalayas of Himachal Pradesh,India

Landslide zonation studies emphasize on preparation of landslide hazard zonation maps considering major instability factors contributing to occurrence of landslides. This paper deals with geographic information system-based landslide hazard zonation in mid Himalayas of Himachal Pradesh from Mandi to Kullu by considering nine relevant instability factors to develop the hazard zonation map. Analytical hierarchy process was applied to assign relative weightages over all ranges of instability factors of the slopes in study area. To generate landslide hazard zonation map, layers in geographic information system were created corresponding to each instability factor. An inventory of existing major landslides in the study area was prepared and combined with the landslide hazard zonation map for validation purpose. The validation of the model was made using area under curve technique and reveals good agreement between the produced hazard map and previous landslide inventory with prediction accuracy of 79.08%. The landslide hazard zonation map was classified by natural break classifier into very low hazard, low hazard, moderate hazard, high hazard and very high landslide hazard classes in geographic information system depending upon the frequency of occurrence of landslides in each class. The resultant hazard zonation map shows that 14.30% of the area lies in very high hazard zone followed by 15.97% in high hazard zone. The proposed model provides the best-fit classification using hierarchical approach for the causative factors of landslides having complex structure. The developed hazard zonation map is useful for landslide preparedness, land-use planning, and social-economic and sustainable development of the region.     

A landslide database for Nicaragua: a tool for landslide-hazard management

A digital landslide database has been created for Nicaragua to provide the scientific community and national authorities with a tool for landslide-hazard assessment, emergency management, land-use planning, development of early warning systems, and the implementation of public and private policies. The Instituto Nicaragüense de Estudios Territoriales (Nicaraguan Geosciences Institute, INETER) began to compile the database in a digital format in 2003 as part of a comprehensive geographical information system for all types of geohazards. Landslide data have been obtained from a variety of sources including newspapers, technical reports, and landslide inventory maps. Inventory maps are largely based on fieldwork and aerial-photo analyses conducted by foreign development agencies in collaboration with INETER and other Nicaraguan institutions. This paper presents the sources of landslide information, introduces the database, and presents the first analyses of the data at national and regional scales. The database currently contains spatial information for about 17,000 landslides that occurred in mountainous and volcanic terrains. Information is mainly recorded for the period 1826–2003, with a large number of events that occurred during the disastrous Hurricane Mitch in October 1998. The oldest historical event is dated at 1570, some events are recorded as prehistorical, and other events have unknown dates of occurrence. Debris flows have been the most common types of landslides, both in volcanic and nonvolcanic areas, but other types, including rockfalls and slides, have also been identified. Intense and prolonged rainfall, often associated with tropical cyclones, and seismic and volcanic activity represent the most important landslide triggers. At a regional scale, the influence of topographic (elevation, slope angle, slope aspect) and lithologic parameters on the occurrence of landslides was analyzed. The development of the database allowed us to define the state of knowledge on landslide processes in the Nicaragua and to provide a preliminary identification of areas affected by landslides.     

Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey

Natural Hazards - Landslide susceptibility maps provide crucial information that helps local authorities, public institutions, and land-use planners make the correct decisions when they are...     

Landslide risk analysis and its application in regional planning: an example from the highlands of the Outer Western Carpathians, Czech Republic

Using detailed field mapping, an analysis of landslide risk has been undertaken in the flysch highlands of the Outer Western Carpathians. The standardized Czech methodology of expert derived susceptibility zonation widely used for land development planning purposes and deterministic modeling of shallow landslides was used to separately assess the susceptibility of different landslide types. The two susceptibility zonation maps were used to define landslide hazard using information about landslide reactivation and the return periods of precipitation that triggered the respective landslide types. A risk matrix was then used to qualitatively analyze the landslide risk to selected assets. The monetary value of these assets, according to actual market prices, was calculated and analyzed with respect to the risk classification. Since the study area is an important residential and recreational area, the practical application of the derived results was checked through a series of interviews conducted with personnel of the local government planning and construction office. This demonstrated a willingness to apply the landslide hazard maps as well as restraints of its successful application. The main one is the absence of legally binding regulations to enforce the spatial planers to use this information.     

云南小江流域滑坡关键影响因子研究

确定诱发滑坡失稳的关键因素是滑坡研究的一个重要内容。采用不同影响因子图层进行危险性分区结果存在明显差异,这是由于第一因子对于滑坡变形失稳的贡献程度不同,即不同影响因子与滑坡的相关性不同。在进行滑坡灾害分析时,必须首先确定影响滑坡的关键因子以建立准确的统计分析模型。采用滑坡确定性系数的合并检验方法,在GIS中对云南小江流域进行了滑坡影响因子分析,并确定了影响滑坡的关键性因子。据此建立的多元统计分析预测模型经检验具有较高精度,要以为小江流域的灾害防治、规划建设提供科学依据。     

Quantitative landslide susceptibility mapping at Pemalang area,Indonesia

For quantitative landslide susceptibility mapping, this study applied and verified a frequency ratio, logistic regression, and artificial neural network models to Pemalang area, Indonesia, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of aerial photographs, satellite imagery, and field surveys; a spatial database was constructed from topographic and geological maps. The factors that influence landslide occurrence, such as slope gradient, slope aspect, curvature of topography, and distance from stream, were calculated from the topographic database. Lithology was extracted and calculated from geologic database. Using these factors, landslide susceptibility indexes were calculated by frequency ratio, logistic regression, and artificial neural network models. Then the landslide susceptibility maps were verified and compared with known landslide locations. The logistic regression model (accuracy 87.36%) had higher prediction accuracy than the frequency ratio (85.60%) and artificial neural network (81.70%) models. The models can be used to reduce hazards associated with landslides and to land-use planning.     

A heuristic approach to global landslide susceptibility mapping

Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.     

WebGIS-based information management system for landslides triggered by Wenchuan earthquake

A digital landslide database has been created for Sichuan province, where a magnitude 8.0 earthquake at 2:28 p.m. on May 12, 2008, to provide the authorities and scientific communities with a tool for landslide risk assessment, emergency management, land-use planning, development of early warning system and enhancement of public awareness of natural hazards. Landslide data have been obtained from a variety of sources including technical reports and landslide inventory maps, and most of which were based on fieldwork and interpretation of aerial photographs. This paper presents the sources of landslide information, database design and the webGIS-based information management system. The database currently contains spatial information for about 9,000 landslides that were mostly triggered by the earthquake. Slide is the most common type of landslide in the database, but other types including rockfall and debris flow have also been identified. The website is an online GIS, providing access to comprehensive landslide information via the Internet. The development of the website allowed us to define the state of knowledge on landslide processes in Sichuan and to provide a preliminary identification of areas affected by landslides.     

Combined use of statistical and DInSAR data analyses to define the state of activity of slow-moving landslides

Statistical analyses have been often used for landslide susceptibility zoning at small to medium scale when relevant base and thematic maps are available. Since the beginning of the last decade, images remotely acquired by spaceborne Synthetic Aperture Radar (SAR) and processed via Differential SAR Interferometry (DInSAR) proved extremely useful for non-invasive and non-destructive monitoring of displacements of the topographic surface. The present paper proposes an original procedure for the definition of the state of activity of slow-moving landslides via the combined use of multivariate statistical analyses and DInSAR data. The procedure is based on the following essential elements: distinction between terrain units used for computational purposes and the final zoning units; independent statistical and DInSAR analyses and activity models leading to first-level state of activity zoning maps; a consistency model between statistical and DInSAR analyses; two confidence and combination models leading, respectively, to second- or third-level state of activity zoning maps. The application in a test area including 19 municipalities in southern Italy, where slow-moving landslides are widespread and accurately mapped by using geomorphological criteria, allowed the generation of the three above-mentioned levels of zoning maps. The results were successfully crosschecked by exploiting a different DInSAR dataset and the results of previous works based on the use of slow-moving landslide-induced damage to facilities surveys.     

Analytic hierarchy process and information value method-based landslide susceptibility mapping and vehicle vulnerability assessment along a highway in Sikkim Himalaya

In hilly areas, highway projects can be a cause of landslides as well as an element of vulnerability due to landslides. Hence, landslide susceptibility mapping of highway corridors can substantially mitigate loss of life and property. For this, a Landslide Susceptibility Assessment Model (LSAM) was developed for a corridor of 27 km along NH 10 in the East Sikkim. Landslide inducing factors viz. Aspect, Distance from Fault, Distance from Road, Drainage Density, Land use and Land cover, Lithology, Plan Curvature, Rainfall, Slope, Soil Depth, and Soil Texture were considered for the study. Results show that areas in proximity to the highway and areas with steeper slope had a higher landslide susceptibility than otherwise. Spatial explicit sensitivity analysis indicated that LSAM was sensitive to distance from the highway and slope. Vehicle vulnerability assessment of base year and horizon years showed that vulnerability increased through time. LSAM is appropriate for hazard mitigation for areas with poor historical data on landslides.     

GIS-based spatial prediction of debris flows using logistic regression and frequency ratio models for Zêzere River basin and its surrounding area,Northwest Covilhã, Portugal

Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate?=?80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.     

Landslide Research at the British Geological Survey:Capture,Storage and Interpretation on a National and Site-Specific Scale

Abstract: Landslide research at the British Geological Survey (BGS) is carried out through a number of activities, including surveying, database development and real-time monitoring of landslides. Landslide mapping across the UK has been carried out since BGS started geological mapping in 1835. Today, BGS geologists use a combination of remote sensing and ground-based investigations to survey landslides. The development of waterproof tablet computers (BGS·SIGMAmobile), with inbuilt GPS and GIS for field data capture provides an accurate and rapid mapping methodology for field surveys. Regional and national mapping of landslides is carried out in conjunction with site-specific monitoring, using terrestrial LiDAR and differential GPS technologies, which BGS has successfully developed for this application. In addition to surface monitoring, BGS is currently developing geophysical ground-imaging systems for landslide monitoring, which provide real-time information on subsurface changes prior to failure events. BGS’s mapping and monitoring activities directly feed into the BGS National Landslide Database, the most extensive source of information on landslides in Great Britain. It currently holds over 14?000 records of landslide events. By combining BGS’s corporate datasets with expert knowledge, BGS has developed a landslide hazard assessment tool, GeoSure, which provides information on the relative landslide hazard susceptibility at national scale.