scholarly journals Assessment of landslide susceptibility for civil protection purposes by means of GIS and statistical analysis: lessons from the Province of Modena, Italy

2017 ◽  
Vol 19 (1) ◽  
pp. 29-43
Author(s):  
LIBERATOSCIOLI Elena ◽  
VAN WESTEN Cees J. ◽  
SOLDATI Mauro
Keyword(s):  
Landslide Susceptibility ◽  
Risk Mitigation ◽  
Mass Movement ◽  
Critical Issue ◽  
Weight Of Evidence ◽  
Slope Position ◽  
Landslide Risk ◽  
Civil Protection ◽  
Multi Temporal ◽  
Position Index

This paper is focused on the analysis of landslide susceptibility for civil protection purposes. A methodology was developed and applied to support measures aiming at landslide risk mitigation. It is based on GIS and the Weight of Evidence (WofE) method, which was preferred among several other statistical approaches because it is suitable for large areas, easy to interpret and simple to program. The latter feature is important for implementing a GIS tool aimed to facilitate Civil Protection in the updating of susceptibility maps. An application of the methodology was performed in a mountainous and hilly area of the Northern Apennines (Italy) located in the Province of Modena where landslides are a critical issue in terms of civil protection due to the recurrent damages to buildings, roads and infrastructures. According to the Region Emilia-Romagna Landslide Inventory Map (RER LIM), shallow slides and earth flows are by far the most widespread mass movement types. Hence, the susceptibility assessment concerned these two types of movements. The choice of the training set, based on active landslides, took into account possible limitations of the input data. The predisposing factors were lithology, slope, curvature, Slope Position Index, aspect, land use, distance from roads. The validation was conducted through the PRC and SRC curves, and direct checking (comparison with past occurrences, multi-temporal orthophotos and field surveys). The resulting models predicted the location of landslides in an acceptable manner. One map for each type of landslides was produced and afterwards they were combined in a single document to improve their intelligibility in a civil protection framework.

scholarly journals MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 461-468
Author(s):  
S. Wiguna ◽  
J. Gao
Keyword(s):  
Landslide Susceptibility ◽  
Total Population ◽  
Critical Role ◽  
Spatial Association ◽  
Source Zone ◽  
Weight Of Evidence ◽  
Population Exposure ◽  
Landslide Risk ◽  
Diverse Range ◽  
Very High

<p><strong>Abstract.</strong> Modelling population exposure to landslide risk is essential for mitigating the damage of landslides. This research aims to assess population exposure to the modelled landslide risk in the Sukabumi region, Indonesia. Also assessed in this study is the importance of 10 environmental variables and their spatial association with past landslide occurrence using the Weight of Evidence (WOE) method. The accuracy of the modelled landslide susceptibility is assessed using the AUC ROC method. Village level population was spatially redistributed via dasymetric modelling, and overlaid with the modelled landslide susceptibility map differentiated by the source zone and the runout zone. It is found that slope, curvature, and soil are the three most influential variables of landslides. The WOE method is able to achieve a similar success rate (0.877) and prediction rate (0.876) in modelling landslide susceptibility. In 2017, medium (114,588 ha) and high (106,337 ha) susceptibility levels were the two largest classes while low (94,778 ha), very high (52,560), and very low (51,910 ha) susceptibility classes are much less extensive. An absolute majority of the population faces a high (1,081,875 people or 38.98% of the total population), and a medium (1,036,080 people or 37.33%) level of landslide risk. Those facing a low (409,658 people or 14.76%), very high (168,193 people or 6.06%), and very low susceptibility (79,656 people or 2.87%) account for slightly more than one fifth of the total population. These findings demonstrate the critical role of GIS in assessing the exposure of population to landslide risk from a diverse range of variables.</p>

scholarly journals GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 162
Author(s):  
Anna Roccati ◽  
Guido Paliaga ◽  
Fabio Luino ◽  
Francesco Faccini ◽  
Laura Turconi
Keyword(s):  
Risk Assessment ◽  
Land Use ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Risk Mitigation ◽  
Current Model ◽  
Susceptibility Mapping ◽  
Mitigation Strategies ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk

Landslide susceptibility mapping is essential for a suitable land use managing and risk assessment. In this work a GIS-based approach has been proposed to map landslide susceptibility in the Portofino promontory, a Mediterranean area that is periodically hit by intense rain events that induce often shallow landslides. Based on over 110 years landslides inventory and experts’ judgements, a semi-quantitative analytical hierarchy process (AHP) method has been applied to assess the role of nine landslide conditioning factors, which include both natural and anthropogenic elements. A separated subset of landslide data has been used to validate the map. Our findings reveal that areas where possible future landslides may occur are larger than those identified in the actual official map adopted in land use and risk management. The way the new map has been compiled seems more oriented towards the possible future landslide scenario, rather than weighting with higher importance the existing landslides as in the current model. The paper provides a useful decision support tool to implement risk mitigation strategies and to better apply land use planning. Allowing to modify factors in order to local features, the proposed methodology may be adopted in different conditions or geographical context featured by rainfall induced landslide risk.

scholarly journals Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245668
Author(s):  
Yanbo Cao ◽  
Xinsheng Wei ◽  
Wen Fan ◽  
Yalin Nan ◽  
Wei Xiong ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Road Network ◽  
River Flow ◽  
Risk Mitigation ◽  
Causative Factor ◽  
Field Investigation ◽  
Weight Of Evidence ◽  
Ratio Method ◽  
Susceptibility Map ◽  
Thematic Layers

The aim of this study is to provide a landslide susceptibility map of the Xunyang District of a mountainous terrain, at the southern part of the Qin-Ba Mountain Region, which has been highly exposed to widely distributed shallow landslides over the past few decades. The Weight of Evidence (WoE) method was adopted in this research considering both the presence of a certain landslide causative factor class and the absence of remaining classes, which was used for determining a clearly spatial correlation between a landslide occurrence and the causative factors. Intrinsic factors, including geomorphological factors, geological factors, and river flow networks, and external factors of anthropogenic engineering activities in terms of density of road network were all considered and involved in the Geological Information System (GIS) environment for reconstructing the thematic layers of factor dataset. Significant assumptions prior to the analysis were emphasized to ensure conditional independence between each pair of factors for this bivariate statistical approach. In addition, a detailed landslide inventory map was constructed through field investigation and a remote sensing interpretation process at a scale of 1:50000. The thematic layers and landslide map were overlapped to obtain a spatial statistical relationship by using the frequency ratio method. At last, the validation process for the derived susceptibility map was conducted by applying the ROC curve, indicating that more than 90% of the landslides were in categories of high and moderate susceptibility zones. The causative factor classes, including the slope angles ranging from 20 to 40°, strong weathered and fractured strata, and road network density were identified to considerably influence the landslide distribution in the study area. The results have proven to be significantly meaningful for landslide hazard risk mitigation and land use management for the local authorities responsible for these fields.

scholarly journals Multi-temporal relative landslide risk analysis for sustainable development of rapidly growing cities

2021 ◽  
Author(s):  
Mariano Di Napoli ◽  
Pietro Miele ◽  
Luigi Guerriero ◽  
Mariagiulia Annibali Corona ◽  
Domenico Calcaterra ◽  
...  
Keyword(s):  
Relative Risk ◽  
Risk Analysis ◽  
Population Growth ◽  
Landslide Susceptibility ◽  
Temporal Analysis ◽  
Population Variation ◽  
Landslide Risk ◽  
Increased Risk ◽  
Multi Temporal ◽  
The City

In the last decades, developing countries have experienced an increase in impact of natural disasters due to both the ongoing climate change and the sustained expansion of urban areas. Intrinsic vulnerability of settlements due to poverty and poor governance, as well as the lack of tools for urban occupation planning and mitigation protocols, have made such impact particularly severe. Cuenca (Ecuador) is a significant example of a city that in the last decades has experienced considerable population growth and an associated increasing of loss due to landslide occurrence. Despite such effects, updated urban planning tools are absent, a condition that suggested an evaluation of multi-temporal relative landslide risk, here presented based on updated data depicting the spatial distribution of landslides and their predisposing factors, as well as population change between 2010 and 2020. In addition, a multi-temporal analysis accounting for risk change between 2010 and 2020 has been carried out. Due to the absence of spatially distributed data about the population, electricity supply contract data have been used as a proxy of the population. Results indicate that current higher relative risk is estimated for municipalities (parroquias) located at the southern sector of the study area (i.e. Turi, Valle, Santa Ana, Tarqui and Paccha). Moreover, the multi-temporal analysis indicates that most municipalities of the city located in the hilly areas that bound the center (i.e. Sayausi, San Joaquin, Tarqui, Valle, Sidcay, Banos, Sidcay, Ricaurte, Paccha and Chiquintad), experiencing sustained population growth, will be exposed to an increased risk with a consistently growing trend. This information is consistent with landslide susceptibility data derived by a machine learning-based analysis that indicate higher susceptibility to landslides in hilly areas surrounding the city center. The obtained relative risk maps can be considered as a useful tool for guiding land-planning, occupation restriction and early warning strategy adoption. The used methodological approach, accounting for landslide susceptibility and population variation through proxy data analysis, has the potential to be applied in a similar context of growing-population cities of low to mid-income countries, where data, usually needed for a comprehensive landslide risk analysis, are only partly available.

scholarly journals Modelling the dynamics of a large rock landslide in the Dolomites (eastern Italian Alps) using multi-temporal DEMs

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5903 ◽  
Author(s):  
Ricarda Gatter ◽  
Marco Cavalli ◽  
Stefano Crema ◽  
Giulia Bossi
Keyword(s):  
Debris Flow ◽  
Risk Mitigation ◽  
Back Analysis ◽  
Debris Avalanche ◽  
Landslide Risk ◽  
Italian Alps ◽  
Landslide Volume ◽  
Multi Temporal ◽  
Seismic Records ◽  
Debris Flow Hazard

Latest advances in topographic data acquisition techniques have greatly enhanced the possibility to analyse landscapes in order to understand the processes that shaped them. High-resolution Digital Elevation Models (DEMs), such as LiDAR-derived ones, provide detailed topographic information. In particular, if multi-temporal DEMs are available, it is possible to carry out a detailed geomorphic change detection analysis. This analysis may provide information about the dynamics of large landslides and may thus, be useful for landslide risk assessments. However, LiDAR-derived DEMs are mostly available only as post-event surveys. The technique is relatively recent, and local or national authorities only started widespread surveys in the last decade. Therefore, it is of a certain interest to analyse the effectiveness of DEMs derived from technical cartography to produce reliable volumetric estimates related to large landslides. This study evaluates the use of a multi-source DEM of Difference (DoD) analysis for the investigation of a large landslide –Le Laste–, which occurred on November 12, 2014 on Mount Antelao (eastern Italian Alps). The landslide initiated as a 365,000 m3rockslide close to the summit of the mountain and transformed into a debris avalanche during its runout. The comparison of pre- and post-event DEMs allowed for the identification and quantification of erosion and deposition areas, and for the estimation of landslide volume. A sound back-analysis of the landslide with the 3D numerical model DAN3D was based on this comparison and on seismic records of the event. These seismic records proved to be remarkably useful, as they allowed for the calibration of the simulated landslide velocity. This ensured the reliability of the model notwithstanding the topographic datasets, intrinsic uncertainties. We found that using a pre-event DEM derived from technical cartography tends to slightly overestimate the volume with respect to the use of the more accurate LiDAR-derived DEM. In recent years, the landslide risk around Mt. Antelao has been increasing alongside the ever-growing population and human activities in the area. Sediment accumulations produced by the Le Laste landslide significantly amplified the debris flow hazard by providing new sediment sources. Therefore, it is crucial to delineate the distribution of this material to enable an adequate debris flow hazard assessment. The material properties derived from the back-analysis of the Le Laste landslide can be used to simulate the runout of possible future events, and to generate reliable hazard zone maps, which are necessary for effective risk mitigation.

scholarly journals InSAR Monitoring of Landslide Activity in Dominica

2021 ◽  
Vol 13 (4) ◽  
pp. 815
Author(s):  
Mary-Anne Fobert ◽  
Vern Singhroy ◽  
John G. Spray
Keyword(s):  
Risk Mitigation ◽  
Disaster Mitigation ◽  
Aerial Images ◽  
Landslide Inventory ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Interferometric Synthetic Aperture Radar ◽  
Rainfall Events ◽  
Susceptibility Maps ◽  
Digital Elevation

Dominica is a geologically young, volcanic island in the eastern Caribbean. Due to its rugged terrain, substantial rainfall, and distinct soil characteristics, it is highly vulnerable to landslides. The dominant triggers of these landslides are hurricanes, tropical storms, and heavy prolonged rainfall events. These events frequently lead to loss of life and the need for a growing portion of the island’s annual budget to cover the considerable cost of reconstruction and recovery. For disaster risk mitigation and landslide risk assessment, landslide inventory and susceptibility maps are essential. Landslide inventory maps record existing landslides and include details on their type, location, spatial extent, and time of occurrence. These data are integrated (when possible) with the landslide trigger and pre-failure slope conditions to generate or validate a susceptibility map. The susceptibility map is used to identify the level of potential landslide risk (low, moderate, or high). In Dominica, these maps are produced using optical satellite and aerial images, digital elevation models, and historic landslide inventory data. This study illustrates the benefits of using satellite Interferometric Synthetic Aperture Radar (InSAR) to refine these maps. Our study shows that when using continuous high-resolution InSAR data, active slopes can be identified and monitored. This information can be used to highlight areas most at risk (for use in validating and updating the susceptibility map), and can constrain the time of occurrence of when the landslide was initiated (for use in landslide inventory mapping). Our study shows that InSAR can be used to assist in the investigation of pre-failure slope conditions. For instance, our initial findings suggest there is more land motion prior to failure on clay soils with gentler slopes than on those with steeper slopes. A greater understanding of pre-failure slope conditions will support the generation of a more dependable susceptibility map. Our study also discusses the integration of InSAR deformation-rate maps and time-series analysis with rainfall data in support of the development of rainfall thresholds for different terrains. The information provided by InSAR can enhance inventory and susceptibility mapping, which will better assist with the island’s current disaster mitigation and resiliency efforts.

Recent technical advancement in natural terrain landslide risk mitigation measures in Hong Kong

2018 ◽  
Vol 25 (2) ◽  
pp. 90-101 ◽  
Author(s):  
Julian S H Kwan ◽  
Harris W K Lam ◽  
Charles W W Ng ◽  
Nelson T K Lam ◽  
S L Chan ◽  
...  
Keyword(s):  
Hong Kong ◽  
Risk Mitigation ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Natural Terrain

scholarly journals A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches

Landslides ◽  
2019 ◽  
Vol 17 (3) ◽  
pp. 627-640 ◽  
Author(s):  
Ting Xiao ◽  
Samuele Segoni ◽  
Lixia Chen ◽  
Kunlong Yin ◽  
Nicola Casagli
Keyword(s):  
Landslide Susceptibility ◽  
Predictive Performance ◽  
Test Site ◽  
Morphological Features ◽  
Landslide Risk ◽  
Simple Method ◽  
Explanatory Variables ◽  
Susceptibility Maps ◽  
Area Under Roc Curve ◽  
Low Altitude

AbstractLandslide susceptibility assessment is vital for landslide risk management and urban planning, and the scientific community is continuously proposing new approaches to map landslide susceptibility, especially by hybridizing state-of-the-art models and by proposing new ones. A common practice in landslide susceptibility studies is to compare (two or more) different models in terms of AUC (area under ROC curve) to assess which one has the best predictive performance. The objective of this paper is to show that the classical scheme of comparison between susceptibility models can be expanded and enriched with substantial geomorphological insights by focusing the comparison on the mapped susceptibility values and investigating the geomorphological reasons of the differences encountered. To this aim, we used four susceptibility maps of the Wanzhou County (China) obtained with four different classification methods (namely, random forest, index of entropy, frequency ratio, and certainty factor). A quantitative comparison of the susceptibility values was carried out on a pixel-by-pixel basis, to reveal systematic spatial patterns in the differences among susceptibility maps; then, those patterns were put in relation with all the explanatory variables used in the susceptibility assessments. The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility were identified. The results shed a new light on the susceptibility models, identifying systematic errors that could be probably associated either to shortcomings of the models or to distinctive morphological features of the test site, such as nearly flat low altitude areas near the main rivers, and some lithological units.

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