Search

The main purpose of this study was to establish a data-driven approach and assess its potential for shallow landslide susceptibility mapping of the Shennongjia Forestry District, China. For the data processing, Fisher segmenting was used for the classification of topographical factors (gradient, relief amplitude, plan curvature, and normalized difference vegetation index) and the improved Otsu method was used to determine the buffer length thresholds of the locational factors (proximity to roads, rivers and faults). The computations show that these two objective methods help to avoid the uncertainty caused by the subjective judgments and the obtained thresholds are more consistent with actual conditions. Considering the different data types, the locational and topographical factors were evaluated by fuzzy comprehensive evaluation while the geological factors were evaluated by frequency index, and then the three separate evaluations were combined using radar chart analysis. The validation result shows that the susceptibility map based on a data-driven approach has a high accuracy of prediction and this approach will be salutary for risk mitigation and land-use management in mountainous regions. [ABSTRACT FROM AUTHOR]

Landslide is a natural hazard that results in many economic damages and human losses every year. Numerous researchers have studied landslide susceptibility mapping (LSM), each attempting to improve the accuracy of the final outputs. However, few studies have been published on the training data selection effects on the LSM. Thus, this study assesses the training landslides random selection effects on support vector machine (SVM) accuracy, logistic regression (LR) and artificial neural networks (ANN) models for LSM in a catchment at the Dodangeh watershed, Mazandaran province, Iran. A 160 landslide locations inventory was collected by Geological Survey of Iran for this investigation. Different methods were implemented to define the landslide locations, such as inventory reports, satellite images and field survey. Moreover, 14 landslide conditioning factors were considered in the analysis of landslide susceptibility. These factors include curvature, plan curvature, profile curvature, altitude, slope angle, slope aspect, distance to faults, distance to stream, topographic wetness index, stream power index, terrain roughness index, sediment transport index, lithology and land use. The results show that the random landslide training data selection affected the parameter estimations of the SVM, LR and ANN algorithms. The results also show that the training samples selection had an effect on the accuracy of the susceptibility model because landslide conditioning factors vary according to the geographic locations in the study area. The LR model was found to be less sensitive than the SVM and ANN models to the training samples selection. Validation results showed that SVM and LR models outperformed the ANN model for all scenarios. The average overall accuracy of LR, SVM and ANN models are 81.42%, 79.82% and 70.2%, respectively. [ABSTRACT FROM AUTHOR]

Despite the support of technological improvements in hazard monitoring and management, the aspect of dissemination of scientific data is still underestimated. Hazard monitoring systems are usually organized in complex networks, composed of automatized procedures in order to capture, pre-process, elaborate, transfer the monitoring result data and to generate warnings. However, a well-organized communication strategy leads to success in hazard management, especially concerning emergency conditions. In this paper, we present an approach for monitoring results dissemination in the form of single-page informative bulletin, composed of synthetic information dedicated to non-expert receivers. We developed an automatic methodology for fast and efficient data analysis and proper elaboration and dissemination, and we integrated all these elements into an algorithm for automatic bulletin generation. Our purpose was to construct a configurable tool that could be easily adapted to the hazard conditions under study. Afterwards, we applied the developed algorithm to landslide monitoring data-set. We validated the proposed approach by verifying the consistency of the model. Our methodology shows how to integrate a variety of information relative to landslide evolution, and it can be a convenient support for the management of natural hazards especially during critical situations. [ABSTRACT FROM AUTHOR]

Early-warning systems (EWSs) are crucial to reduce the risk of landslide, especially where the structural measures are not fully capable of preventing the devastating impact of such an event. Furthermore, designing and successfully implementing a complete landslide EWS is a highly complex task. The main technical challenges are linked to the definition of heterogeneous material properties (geotechnical and geomechanical parameters) as well as a variety of the triggering factors. In addition, real-time data processing creates a significant complexity, since data collection and numerical models for risk assessment are time consuming tasks. Therefore, uncertainties in the physical properties of a landslide together with the data management represent the two crucial deficiencies in an efficient landslide EWS. Within this study the application is explored of the concept of fragility curves to landslides; fragility curves are widely used to simulate systems response to natural hazards, i.e. floods or earthquakes. The application of fragility curves to landslide risk assessment is believed to simplify emergency risk assessment; even though it cannot substitute detailed analysis during peace-time. A simplified risk assessment technique can remove some of the unclear features and decrease data processing time. The method is based on synthetic samples which are used to define the approximate failure thresholds for landslides, taking into account the materials and the piezometric levels. The results are presented in charts. The method presented in this paper, which is called failure index fragility curve (FIFC), allows assessment of the actual real-time risk in a case study that is based on the most appropriate FIFC. The application of an FIFC to a real case is presented as an example. This method to assess the landslide risk is another step towards a more integrated dynamic approach to a potential landslide prevention system. Even if it does not define absolute thresholds, the accuracy is satisfactory for a preliminary risk assessment, and it can provide more lead-time to understand the hazard level in order to make decisions as compared with a more sophisticated numerical approach. Hence, the method is promising to become an effective tool during landslide emergency. [ABSTRACT FROM PUBLISHER]

The main objective of this study is to investigate potential application of an integrated evidential belief function (EBF)-based fuzzy logic model for spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). First, a landslide inventory map was constructed from various sources. Then the landslide inventory map was randomly partitioned as a ratio of 70/30 for training and validation of the models, respectively. Second, six landslide conditioning factors (slope angle, slope aspect, lithology, distance to faults, soil type, land use) were prepared and fuzzy membership values for these factors classes were estimated using the EBF. Subsequently, fuzzy operators were used to generate landslide susceptibility maps. Finally, the susceptibility maps were validated and compared using the validation dataset. The results show that the lowest prediction capability is the fuzzy SUM (76.6%). The prediction capability is almost the same for the fuzzy PRODUCT and fuzzy GAMMA models (79.6%). Compared to the frequency-ratio based fuzzy logic models, the EBF-based fuzzy logic models showed better result in both the success rate and prediction rate. The results from this study may be useful for local planner in areas prone to landslides. The modelling approach can be applied for other areas. [ABSTRACT FROM AUTHOR]