scholarly journals Modeling landslide susceptibility and landslide volume per geomorphologic landform unit

2021 ◽  
Author(s):  
Gabriel Legorreta Paulin ◽  
Rocio Marisol Alanis Anaya ◽  
Trevor Contreras ◽  
Lilia Arana Salinas
Keyword(s):  
Disaster Management ◽  
Power Law ◽  
Landslide Susceptibility ◽  
Empirical Relationship ◽  
Shallow Landslides ◽  
Potential Contribution ◽  
Sediment Production ◽  
Landslide Volume ◽  
Landform Unit ◽  
Landform Units

Abstract The analysis of landslide susceptibility and landslide volumes in landforms can provide information for planning disaster management in an area. Landslide susceptibility per landform unit and the potential contribution of material delivered from each unit was calculated for a 105 km2 watershed on the south flank of Pico de Orizaba volcano, Mexico. The landslide susceptibility is calculated from the area and frequency of landslides. The volume is obtained from detailed geometric values of shallow landslides in order to establish an empirical relationship that takes the form of a power law from which the potential volume of all shallow landslides is calculated for the watershed. The study shows that most of the landslides are on volcanic landform units; however, the landslides in sedimentary units contribute more sediments per square kilometer. It also shows that landform units can be used to explain the predisposition and variability of landslide sediment production for a large and complex geological watershed.

scholarly journals Evaluating performances of simplified physically based models for landslide susceptibility

2015 ◽  
Vol 12 (12) ◽  
pp. 13217-13256 ◽  
Author(s):  
G. Formetta ◽  
G. Capparelli ◽  
P. Versace
Keyword(s):  
Landslide Susceptibility ◽  
Goodness Of Fit ◽  
Shallow Landslides ◽  
Model Parameters ◽  
Test Case ◽  
Susceptibility Analysis ◽  
Physically Based ◽  
Private And Public ◽  
Physically Based Models

Abstract. Rainfall induced shallow landslides cause loss of life and significant damages involving private and public properties, transportation system, etc. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. Reliable models' applications involve: automatic parameters calibration, objective quantification of the quality of susceptibility maps, model sensitivity analysis. This paper presents a methodology to systemically and objectively calibrate, verify and compare different models and different models performances indicators in order to individuate and eventually select the models whose behaviors are more reliable for a certain case study. The procedure was implemented in package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, the optimization of the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with model M3 is the best modeling solution for our test case.

scholarly journals A method for predicting the factor of safety of an infinite slope based on the depth ratio of the wetting front

2015 ◽  
Vol 3 (1) ◽  
pp. 791-836 ◽  
Author(s):  
B.-G. Chae ◽  
J.-H. Lee ◽  
H.-J. Park ◽  
J. Choi
Keyword(s):  
Steady State ◽  
Slope Stability ◽  
Unsaturated Soil ◽  
Landslide Susceptibility ◽  
Shallow Landslides ◽  
Rainfall Infiltration ◽  
Wetting Front ◽  
Operating Characteristics ◽  
Infinite Slope ◽  
Depth Ratio

Abstract. Most landslides in Korea are classified as shallow landslides with an average depth of less than 2 m. These shallow landslides are associated with the advance of a wetting front in the unsaturated soil due to rainfall infiltration, which results in an increase in water content and a reduction in the matric suction in the soil. Therefore, this study presents a modified equation of infinite slope stability analysis based on the concept of the saturation depth ratio to analyze the slope stability change associated with the rainfall on a slope. A rainfall infiltration test in unsaturated soil was performed using a column to develop an understanding of the effect of the saturation depth ratio following rainfall infiltration. The results indicated that the rainfall infiltration velocity due to the increase in rainfall in the soil layer was faster when the rainfall intensity increased. In addition, the rainfall infiltration velocity tends to decrease with increases in the unit weight of soil. The proposed model was applied to assess its feasibility and to develop a regional landslide susceptibility map using a Geographic Information System (GIS). For that purpose, the spatial databases for input parameters were constructed and landslide locations were obtained. In order to validate the proposed approach, the results of the proposed approach were compared with the landslide inventory using ROC (Receiver Operating Characteristics) graph. In addition, the results of the proposed approach were compared with the previous approach used steady state hydrological model. Consequently, the approach proposed in this study displayed satisfactory performance in classifying landslide susceptibility and showed better performance than the steady state approach.

scholarly journals AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1819
Author(s):  
Tianjun Qi ◽  
Yan Zhao ◽  
Xingmin Meng ◽  
Guan Chen ◽  
Tom Dijkstra
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Heavy Rainfall ◽  
Human Life ◽  
Relative Weight ◽  
Shallow Landslides ◽  
Mitigation Measures ◽  
Slope Aspect ◽  
Economic Damage ◽  
Geological Conditions

Groups of landslides induced by heavy rainfall are widely distributed on a global basis and they usually result in major losses of human life and economic damage. However, compared with landslides induced by earthquakes, inventories of landslides induced by heavy rainfall are much less common. In this study we used high-precision remote sensing images before and after continuous heavy rainfall in southern Tianshui, China, from 20 June to 25 July 2013, to produce an inventory of 14,397 shallow landslides. Based on the results of landslide inventory, we utilized machine learning and the geographic information system (GIS) to map landslide susceptibility in this area and evaluated the relative weight of various factors affecting landslide development. First, 18 variables related to geomorphic conditions, slope material, geological conditions, and human activities were selected through collinearity analysis; second, 21 selected machine learning models were trained and optimized in the Python environment to evaluate the susceptibility of landslides. The results showed that the ExtraTrees model was the most effective for landslide susceptibility assessment, with an accuracy of 0.91. This predictive ability means that our landslide susceptibility results can be used in the implementation of landslide prevention and mitigation measures in the region. Analysis of the importance of the factors showed that the contribution of slope aspect (SA) was significantly higher than that of the other factors, followed by planar curvature (PLC), distance to river (DR), distance to fault (DTF), normalized difference vehicle index (NDVI), distance to road (DTR), and other factors. We conclude that factors related to geomorphic conditions are principally responsible for controlling landslide susceptibility in the study area.

scholarly journals The Distribution of Salted Groundwater and the Impact to Settlement Areas in Grogol Sub District

2004 ◽  
Vol 13 (2) ◽  
Author(s):  
S Suharjo
Keyword(s):  
Ground Water ◽  
Research Area ◽  
Water Salinity ◽  
Distribution Maps ◽  
Factors Influencing ◽  
Landform Unit ◽  
Salinity Data ◽  
Deposition Processes ◽  
The Impact ◽  
Landform Units

This research is aimed at studying the ground water salinity and the geographicaal aspect of Grogol subdistrict area. This study, therefore, dials withs (a) the ground water salinity and the factors influencing; (b) the classes of land suitability for settlement in Grogol subdistrict; and (c) the influences of the ground water salinity to the suitability of sattlement areas. This research put its emphasis on the geomorphological approach and uses lends units as the basis of its studt. The research area consists of four landform units and nine land units. From land unit maps, we can make the distribution maps of the ground water salinity and its influences to the suitability between settlement and the level suitability for settlement data. The ground water salinity data are obtained by measuring in the field and laboratory analysis. The result of this research shows that the distribution of the ground water salinity is located in the landform unit of the old floid and in the deposition processes. The distribution of the ground water salinity in the research area does not influence the growth pattern of settlement but influences the physical building.

scholarly journals A strategy for GIS-based 3-D slope stability modelling over large areas

2014 ◽  
Vol 7 (4) ◽  
pp. 5407-5445 ◽  
Author(s):  
M. Mergili ◽  
I. Marchesini ◽  
M. Alvioli ◽  
M. Metz ◽  
B. Schneider-Muntau ◽  
...  
Keyword(s):  
Slope Stability ◽  
Landslide Susceptibility ◽  
Slope Failure ◽  
Computing Time ◽  
Central Italy ◽  
Shallow Landslides ◽  
Landslide Susceptibility Mapping ◽  
Deterministic Models ◽  
Average Value ◽  
Slip Surfaces

Abstract. GIS-based deterministic models may be used for landslide susceptibility mapping over large areas. However, such efforts require specific strategies to (i) keep computing time at an acceptable level, and (ii) parameterize the geotechnical data. We test and optimize the performance of the GIS-based, 3-D slope stability model r.slope.stability in terms of computing time and model results. The model was developed as a C- and Python-based raster module of the open source software GRASS GIS and considers the 3-D geometry of the sliding surface. It calculates the factor of safety (FoS) and the probability of slope failure (Pf) for a number of randomly selected potential slip surfaces, ellipsoidal or truncated in shape. Model input consists of a DEM, ranges of geotechnical parameter values derived from laboratory tests, and a range of possible soil depths estimated in the field. Probability density functions are exploited to assign Pf to each ellipsoid. The model calculates for each pixel multiple values of FoS and Pf corresponding to different sliding surfaces. The minimum value of FoS and the maximum value of Pf for each pixel give an estimate of the landslide susceptibility in the study area. Optionally, r.slope.stability is able to split the study area into a defined number of tiles, allowing parallel processing of the model on the given area. Focusing on shallow landslides, we show how multi-core processing allows to reduce computing times by a factor larger than 20 in the study area. We further demonstrate how the number of random slip surfaces and the sampling of parameters influence the average value of Pf and the capacity of r.slope.stability to predict the observed patterns of shallow landslides in the 89.5 km2 Collazzone area in Umbria, central Italy.

A combined procedure to assess rainfall-induced shallow landslide detachment, transit and runout susceptibility using Machine Learning and GIS techniques

2021 ◽  
Author(s):  
Mariano Di Napoli ◽  
Diego Di Martire ◽  
Domenico Calcaterra ◽  
Marco Firpo ◽  
Giacomo Pepe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Mitigation Measures ◽  
Economic Losses ◽  
Meaningful Improvement ◽  
Susceptibility Evaluation ◽  
Landslide Runout

<p>Rainfall-induced landslides are notoriously dangerous phenomena which can cause a notable death toll as well as major economic losses globally. Usually, shallow landslides are triggered by prolonged or severe rainfalls and frequently may evolve into potentially catastrophic flow-like movements. Shallow failures are typical in hilly and mountainous areas due to the combination of several predisposing factors such as slope morphology, geological and structural setting, mechanical properties of soils, hydrological and hydrogeological conditions, land-use changes and wildfires. Because of the ability of these phenomena to travel long distances, buildings and infrastructures located in areas improperly deemed safe can be affected.</p><p>Spatial and temporal hazard posed by flow-like movements is due to both source characteristics (e.g., location and volume) and the successive runout dynamics (e.g., travelled paths and distances). Hence, the assessment of shallow landslide susceptibility has to take into account not only the recognition of the most probable landslide source areas, but also  landslide runout (i.e., travel distance). In recent years, a meaningful improvement in landslide detachment susceptibility evaluation has been gained through robust scientific advances, especially by using statistical approaches. Furthermore, various techniques are available for landslide runout susceptibility assessment in quantitative terms. The combination of landslide detachment and runout dynamics has been admitted by many researchers as a suitable and complete procedure for landslide susceptibility evaluation. However, despite its significance, runout assessment is not as widespread in literature as landslide detachment assessment and still remains a challenge for researchers. Currently, only a few studies focus on the assement of both landslide detachment susceptibility (LDS) and landslide runout susceptibility (LRS).</p><p>In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. Such procedure is based on the integration between LDS assessment via Machine Learning techniques (applying the Ensemble approach) and LRS assessment through GIS-based tools (using the “reach angle” method). This methodology has been applied to the Cinque Terre National Park (Liguria, north-west Italy), where risk posed by flow-like movements is very high. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. In particular, the obtained map may be useful for urban and regional planning, as well as for decision-makers and stakeholders, to predict areas that may be affected by rainfall-induced shallow landslides  in the future and to identify areas where risk mitigation measures are needed.</p>

Disaster management in power-law networks: Recovery from and protection against intentional attacks

2007 ◽  
Vol 381 ◽  
pp. 497-514 ◽  
Author(s):  
Behnam A. Rezaei ◽  
Nima Sarshar ◽  
Vwani P. Roychowdhury ◽  
P. Oscar Boykin
Keyword(s):  
Disaster Management ◽  
Power Law

Topographic Types Influence on the Landslide Movement

2011 ◽  
Vol 243-249 ◽  
pp. 5258-5262 ◽  
Author(s):  
Xiao Yi Fan ◽  
Meng Han
Keyword(s):  
Friction Coefficient ◽  
Power Law ◽  
Field Investigation ◽  
Seismic Landslides ◽  
Landslide Volume ◽  
Influence Coefficients ◽  
Topographic Influence ◽  
Landslide Movement

According to the 95 landslides of field investigation and literatures, the topographic types of landslide movement were divided into river, ladder and linearity. Based on the power-law relationship between the volume and equivalent friction coefficient of non-block landslides, the topographic influence coefficients were studied which were influenced by the landslide volumes and occurrence mechanisms. Because of different volumes of seismic landslides and rainfall landslides, the influence coefficients of topography were significant different. It indicated that the disaster-causing mechanism of landslides not only closely related with the landslide volume, but also were controlled by topographic types and occurrence mechanisms.

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