Climate disaster resilience of the education sector in Thua Thien Hue Province, Central Vietnam

Recognizing the importance of building disaster resilience for education sector, this study aims to develop a methodology to measure the level of educational resilience to cope with natural disasters and is then applied in Central Vietnam. The assessment tool in this paper is developed through a combination of climate disaster resilience indexes and the 16 tasks of Hyogo framework for action designed for education sector. It looks at five dimensions namely physical conditions, human resources, institutional issues, external relationships, and natural conditions, with each dimension characterized by three parameters and five variables. Findings from this study provide important insights into enhancing resilience of the education system in Thua Thien Hue at the provincial, local, and school levels. By giving the overall resilience situation, it can help policy-makers and practitioners in developing an effective plan to increase the level of educational resilience. In addition, it provides the School Management Board with a means to assess the school??s resilience level and set out priorities that need to be focused on with regard to the improvement of school safety and disaster risk reduction education.     

The October 13, 2020, deadly rapid landslide triggered by heavy rainfall in Phong Dien,Thua Thien Hue,Vietnam

Landslides - At about 12:00 a.m., on October 13, 2020, a rapid rotational landslide induced by rainfall swept over Ranger Station-7 in Phong Xuan commune, Phong Dien district, Thua Thien Hue...     

Slope stability analysis using a physically based model: a case study from A Luoi district in Thua Thien-Hue Province,Vietnam

A case study of slope stability mapping is presented for the A Luoi district situated in the mountainous western part of Thua Thien-Hue Province in Central Vietnam, where slope failures occur frequently and seriously affect local living conditions. The methodology is based on the infinite slope stability model, which calculates a safety factor as the ratio between shear strength and shear stress. The triggering mechanism for slope instability considered in the analysis is the maximum daily precipitation recorded in a 28-year period (1976–2003) taking into account runoff and infiltration predicted with a hydrological model. All necessary physical parameters are derived from topography, soil texture, and land use, in GIS-raster grid format with pixel size of 30 by 30 m. Results of the analysis are compared with a slope failure inventory map of 2001, showing that more than 86.9 % of the existing slope failures are well predicted by the physically based slope stability model. It can be concluded that the larger part of the study area is prone to landsliding. The resulting slope stability map is useful for further research and land-use planning, but for precise prediction of future slope failures, more effort is needed with respect to spatial variation of causative factors and analysis techniques.     

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.     

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.     

Validation of an artificial neural network model for landslide susceptibility mapping

The aim of this study was to validate an artificial neural network model at Youngin, Janghung, and Boeun, Korea, using the geographic information system (GIS). The factors that influence landslide occurrence, such as the slope, aspect, curvature, and geomorphology of topography, the type, material, drainage, and effective thickness of soil, the type, diameter, age, and density of forest, distance from lineament, and land cover were either calculated or extracted from the spatial database and Landsat TM satellite images. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the artificial neural network model. The landslide susceptibility analysis results were validated and cross-validated using the landslide locations as study areas. For this purpose, weights for each study area were calculated by the artificial neural network model. Among the nine cases, the best accuracy (81.36%) was obtained in the case of the Boeun-based Janghung weight, whereas the Janghung-based Youngin weight showed the worst accuracy (71.72%).     

Application of analytical hierarchy process and least-squares method for landslide susceptibility assessment along the Zhong-Wu natural gas pipeline, China

As one of the major problems of geo-engineering, landslides often influence the safety of linear engineering projects that cross mountainous areas. Therefore, when selecting suitable routes for such projects, it is important to assess their susceptibility to landslides. In this paper, we used a natural gas pipeline in the northeast of the Yunnan-Guizhou Plateau of China as a case study to analyze landslide susceptibility. Based on engineering geological analogy, the analytical hierarchy process, and the least-squares method, a regional landslide susceptibility assessment model was developed and was programmed using GIS ArcEngine components under the Visual Studio.NET environment. The landslide susceptibility along the Zhong-Wu natural gas pipeline from Zhongxian County to Wuhan was assessed based on this model and classified into five levels: very safe, safe, moderate, susceptible, and very susceptible. The high accuracy and prediction capability of the model were confirmed by comparing the model results with past landslide data and performing a prediction test. The results indicated that the assessment model used in this study is reliable and can be used for landslide susceptibility assessment and route selection in other areas.     

A multi-method approach to study the stability of natural slopes and landslide susceptibility mapping

In this paper, a multi-method approach for the assessment of the stability of natural slopes and landslide hazard mapping applied to the Dakar coastal region is presented. This approach is based on the effective combination of geotechnical field and laboratory works, of GIS, and of mechanical (deterministic and numerical) stability analysis. By using this approach, valuable results were gained regarding instability factors, landslide kinematics, simulation of slope failure and coastal erosion. This led to a thorough assessment and strong reduction in the subjectivity of the slope stability and hazard assessment and to the development of an objective landslide danger map of the SW coast of Dakar. Analysis of the results shows that the slides were influenced by the geotechnical properties of the soil, the weathering, the hydrogeological situation, and the erosion by waves. The landslide susceptibility assessment based on this methodological approach has allowed for an appropriate and adequate consideration of the multiple factors affecting the stability and the optimization of planning and investment for land development in the city.     

A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method

In spite of its small size, Japan suffers many landslide disasters due to intense rainfall and earthquakes. This article describes the distribution and topography of these landslides, and a new method of evaluating the susceptibility, the analytical hierarchic process (AHP). The method assigns scores to each factor of micro-topography of landslide-prone areas identified in aerial photographs, and assesses the susceptibility of landslide from the total score. In addition, a method of simulating sliding mass runout is briefly presented for the designating sediment-related disaster warning areas.     

Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China

The main purpose of this paper is to present the use of multi-resource remote sensing data, an incomplete landslide inventory, GIS technique and logistic regression model for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Landslide location polygons were delineated from visual interpretation of aerial photographs, satellite images in high resolutions, and verified by selecting field investigations. Eight factors, including slope angle, slope aspect, elevation, distance from drainages, distance from roads, distance from main faults, seismic intensity and lithology were selected as controlling factors for earthquake-triggered landslide susceptibility mapping. Qualitative susceptibility analyses were carried out using the map overlaying techniques in GIS platform. The validation result showed a success rate of 82.751 % between the susceptibility probability index map and the location of the initial landslide inventory. The predictive rate of 86.930 % was obtained by comparing the additional landslide polygons and the landslide susceptibility probability index map. Both the success rate and the predictive rate show sufficient agreement between the landslide susceptibility map and the existing landslide data, and good predictive power for spatial prediction of the earthquake-triggered landslides.     

Analytic hierarchy process applied to landslide susceptibility mapping of the North Branch of Argentino Lake,Argentina

Natural Hazards - In the present study, we achieved the susceptibility mapping to slope instability processes by the implementation of Analytic Hierarchy Process and Weighted Linear Combination...     

Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians,Romania

The goal of this paper is to assess the landslide susceptibility of a hilly area in the Subcarpathian sector of the Prahova Valley, using the weight of evidence statistical method. This method aims to reduce the multitude of landslide-related conditions to a pattern of a few discrete predictive variables. The method is based on the decision of which state is more likely to occur grounded on the presence or absence of a predictive variable and the occurrence of an event (e.g., landslide) within a pixel. Based on the chi-square test and the Pearson correlation applied on the data, the selected conditionally independent variables in this study were as follows: slope gradient, slope aspect, and land use. Weights calculated individually for the three themes were added to produce a probability estimate of the area. The predictive power of the map was tested on the basis of a split sample of landslides that were not used in the modeling process. The fact that a great percent of the declivitous surfaces are susceptible to landslides shows the dominant manner of the evolution of the Subcarpathian slopes, the acceleration or deceleration of the process being influenced by the land use.     

Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region,Uttarakhand

A comprehensive use of analytical hierarchy process (AHP) method in landslide susceptibility mapping (LSM) has been presented for rim region of Tehri reservoir. Using remote sensing data, various landslide causative factors responsible for inducing instability in the area were derived. Ancillary data such as geological map, soil map, and topographic map were also considered along with remote sensing data. Exhaustive field checks were performed to define the credibility of the random landslide conditioning factors considered in this study. Apart from universally acceptable inherent causative factors used in the susceptibility mapping, others such as impact of reservoir impoundment on terrain, topographic wetness index and stream power index were found to be important causative factors in rim region of the Tehri reservoir. The AHP method was used to acquire weights of factors and their classes respectively. Weights achieved from AHP method matched with the existing field conditions. Acceptable consistency ratio (CR) value was achieved for each AHP matrix. Weights of each factor were integrated with weighted sum technique and a landslide susceptibility index map was generated. Jenk’s natural break classifier was used to classify LSI map into very low, low, moderate, high and very high landslide susceptible classes. Validation of the susceptibility map was performed using cumulative percentage/success rate curve technique. Area under curve value of the success rate curve was converted to percentage validation accuracy and a reasonable 78.7% validation accuracy was achieved.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

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.     

A multi-analysis remote-sensing approach for mapping groundwater resources in the karstic Meo Vac Valley, Vietnam

Remote sensing data can be integrated with analyses of topography, structural geology, hydrogeology and geophysics. The integration gives premises for the delineation of zones of potential groundwater resources in strongly fractured and karstified deep aquifers in the uplifted Meo Vac Highland, northern Vietnam. Remote sensing analysis outlines geological faults with hydrogeological significance. These faults are combined with a derived lineament density map, interpreted analysis of surface flow direction and existing hydrogeological data, resulting in indications of groundwater flow direction. An analysis of the SPOT 5 band ratio 4/1, together with indications of surface-flow direction in low terrains, results in a determination of underground cavern passages. The delineated zones of potential groundwater resources are verified by detailed hydrogeological field surveys and geophysical measurements. Remote sensing analysis is shown to effectively contribute to the investigation of groundwater resources for a hydrogeologically complex area.     

GIS based landslide susceptibility mapping using a fuzzy logic approach: A case study from Ghurmi-Dhad Khola area, Eastern Nepal

Landslides cause extensive loss of life and property in the Nepal Himalaya. Since the late 1980s, different mathematical models have been developed and applied for landslide susceptibility mapping and hazard assessment in Nepal. The main goal of this paper is to apply fuzzy logic to landslide susceptibility mapping in the Ghurmi-Dhad Khola area, Eastern Nepal. Seven causative factors are considered: slope angle, slope aspect, distance from drainage, land use, geology, distance from faults and folds, soil and rock type. Likelihood ratios are obtained for each class of causative factors by comparison with past landslide occurrences. The ratios are normalized between zero and one to obtain fuzzy membership values. Further, different fuzzy operators are applied to generate landslide susceptibility maps. Comparison with the landslide inventory map reveals that the fuzzy gamma operator with a γ-value of 0.60 yields the best prediction accuracy. Consequently, this operator is used to produce the final landslide susceptibility zonation map.