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Geologically, La Paz City is located in an unstable area. During the history of La Paz city, many landslides have destroyed houses and valuable infrastructures. In the last decades, time series Interferometric Synthetic Aperture Radar (InSAR) technologies have demonstrated a great capacity for detecting slow ground displacement, achieving an accuracy of millimetre-level. In order to have a better landslide monitoring of La Paz city, in this study, the Sentinel-1 SAR images have been processed by Persistent Scatterer Interferometry (PSI) and the Small Baseline Subset (SBAS) techniques. The time span of the datasets is from March 2015 to August 2016. Both ascending and descending Synthetic Aperture Radar (SAR) images have been processed to obtain the line of sight (LOS) ground velocity, and then the results have been combined to estimate the up-down and east-west displacement. Several active movement areas have been identified, showing a surface velocity up to 158 mm year−1 westward and 49 mm year−1 eastward. Furthermore, two important findings have been discovered. First, the InSAR result has detected movement in Auquisamaa hill before the area collapsed (15 February 2017), where five houses are buried. Second, the InSAR result has identified that there are still some unstable sites in Callapa area, where a mega-landslide has destroyed more than a thousand of houses in February 2011. In conclusion, we have verified that the InSAR technology could be a very useful tool to help La Paz public institutions for a better management of urban planning, landslide areas delimitation and landslide risk mitigation. [ABSTRACT FROM AUTHOR]

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]

We mapped landslide susceptibility in the provinces of Lucca, Pistoia and Prato (central Italy), a 3103 km2territory that approximately corresponds to the portion of Tuscany principally affected by landslides. We used a methodology based on a treebagger random forest. The input parameters used for the susceptibility assessment are curvature, flow accumulation, topographic wetness index, elevation, profile curvature, planar curvature, slope gradient, aspect, land use and lithology. The map was validated providing satisfactory results (AUC = 0.84). The map classifies the study area into four susceptibility classes that identify areas with different probabilities of being affected by landslides. TheMain Maprepresents a useful instrument to assist land planning, development of mitigation measures and landslide risk management. Moreover, it could be used in further research addressing quantitative hazard and risk assessment. [ABSTRACT FROM AUTHOR]

The study area of the Costa Viola mountain ridge is strongly exposed to shallow landslides triggered by rainfall. Starting from a susceptibility map, recently published by the same team, the related risk has been assessed, limitedly to landslide sources. The hazard has first been evaluated by considering the recurrence periods of triggering events. Economic value and physical vulnerability of the types of elements at risk have been assumed by considering land-use and the effects of similar events in Southern Italy, respectively. The 1:30,000-scale risk map (Main Map) has been produced by combining the above-mentioned layers. Low risk prevails in the study area, whilst the highest values are concentrated along the coast, where villages and the main infrastructure are located. The expected annual damage exceeds 570 million € (about 68% pertaining to low-risk cells). Risk maps produced through similar simplified geographic information system-based procedures may play a crucial role in accurate land-use planning and effective decision-making. [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]

Landslides are one of the most destructive disasters that cause damage to both property and life every year. The mountainous terrain, heavy and prolonged rainfall and dense development near steep hillsides make Hong Kong as one of the most vulnerable metropolitans to the risk of landslides. Therefore, regional-specific landslide susceptibility assessment in Hong Kong is necessary for hazard management and effective land use planning. Various methodologies have been proposed for landslide susceptibility mappings. By applying the statistically based frequency ratio and logistic regression models in Lantau Island, Hong Kong, this study attempts to add extra value to the literature of evaluating their ‘prediction rate’ (rather than ‘success rate’) for landslide susceptibility mapping in a temporal context. The spatial relationship among landslide occurrence and nine causative factors (elevation, slope, aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to river, distance to fault and lithology) were examined, and landslide susceptibility maps were generated by frequency ratio and logistic regression model. Validations of the mapping results were performed by calculating relative operating characteristics (ROC). Virtually all previous studies randomly extracted and reserved a portion of historical landslide records to perform the model evaluation. In contrast, in this study, 1864 (70%) landslide records in the Enhanced Natural Terrain Landslide Inventory from 2000 to 2008 are utilized to train the model, while the subsequent 799 (30%) landslides occurred from 2008 to 2009 are used for model validation. The validation result shows that logistic regression model (84.05%) possesses a better prediction power than frequency ratio model (76.64%) for the study area. This study attempts to offer a new model assessment strategy for landslide susceptibility mapping. The resultant maps also provide a scientific assessment of the risk areas with respect to landslides on Lantau Island and could serve as a basis for decisions or justification of the Lantau development planning. [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]

A decision tree approach was applied and validated for analysis of landslide susceptibility using a geographic information system (GIS). The study area was the Pyeongchang area in Gangwon Province, Korea, where many landslides occurred in 2006 and where the 2018 Winter Olympics are to be held. Spatial data, such as landslides, topography, and geology, were detected, collected, and compiled in a database using remote sensing and GIS. The 3994 recorded landslide locations were randomly split 50/50 for training and validation of the models. A decision tree model, which is a type of data-mining classification model, was applied and decision trees were constructed using the chi-squared (χ2) automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. Also, as a reference, a frequency-ratio model was applied using the same database. The relationships between the detected landslide locations and their factors were identified and quantified by frequency-ratio and decision tree models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indices and maps. Then, the resulting landslide-susceptibility maps were validated using area-under-the-curve (AUC) analysis with the landslide area data that had not been used for training the model. The decision tree models using the CHAID and QUEST algorithms had accuracies of 81.56% and 80.91%, respectively, which were somewhat better than the results for the frequency-ratio model (80.15%). These results indicate that decision tree models using the CHAID and QUEST algorithms can be useful for landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

The representation and modelling of physical and natural systems are mostly implemented using partial differential equations. Physical processes such as flooding, tsunamis, avalanches, earthquakes and landslides undeniably possess complex systems behaviour. Modelling such dynamic phenomena can be adequately addressed by using geocomplexity – complex systems theory and cellular automata (CA). This study develops a novel approach that couples geographic information systems (GIS), multicriteria evaluation (MCE) and CA to simulate shallow landslide flows occurring in urban areas. The landslide susceptibility map produced from the MCE model was used as one input for the CA model. The high-resolution digital elevation model (DEM) is used to calculate topographic variables such as slope gradient, aspect gradient, stream power index, topographic wetness index, and flow direction which were all used in model design. The developed MCE-CA simulation model was tested on historical landslide data in Metro Vancouver, Canada. The spatial extents of the landslide simulations were compared with actual data to test the model simulation outcomes. The developed model has a potential to become useful tool that can aid urban planners and emergency workers in identifying and mitigating threats due to landslides. [ABSTRACT FROM PUBLISHER]