Landslide risk perception in Frosinone (Lazio, Central Italy)

Gravina, Teresita; Figliozzi, E.; Mari, Nicola; Schinosa, Francesca De Luca Tupputi

doi:10.1007/s10346-016-0787-2

Cited by 22 publications

(12 citation statements)

References 19 publications

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“…During recent decades, many studies on landslide susceptibility mapping have been conducted in various parts of the world. Researchers have applied different approaches to produce landslide susceptibility maps, such as statistical models, probabilistic models, knowledge-driven models, and machine learning models using geographical information systems and remote sensing techniques like the analytical hierarchy process (AHP) and bivariate statistics [9,18], logistic regression (LR), artificial neural networks (ANN), frequency ratio (FR), naive bayes classifier, auto logistic modeling, static methods, multivariate adaptive regression, two-class kernel logistic regression, SVM, artificial neural network kernel, logistic regression and logistic tree, random forest, and decision tree methods [19]. Ensemble techniques have been shown to achieve better results than a single method.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

et al. 2019

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Landslides are among the most harmful natural hazards for human beings. This study aims to delineate landslide hazard zones in the Darjeeling and Kalimpong districts of West Bengal, India using a novel ensemble approach combining the weight-of-evidence (WofE) and support vector machine (SVM) techniques with remote sensing datasets and geographic information systems (GIS). The study area currently faces severe landslide problems, causing fatalities and losses of property. In the present study, the landslide inventory database was prepared using Google Earth imagery, and a field investigation carried out with a global positioning system (GPS). Of the 326 landslides in the inventory, 98 landslides (30%) were used for validation, and 228 landslides (70%) were used for modeling purposes. The landslide conditioning factors of elevation, rainfall, slope, aspect, geomorphology, geology, soil texture, land use/land cover (LULC), normalized differential vegetation index (NDVI), topographic wetness index (TWI), sediment transportation index (STI), stream power index (SPI), and seismic zone maps were used as independent variables in the modeling process. The weight-of-evidence and SVM techniques were ensembled and used to prepare landslide susceptibility maps (LSMs) with the help of remote sensing (RS) data and geographical information systems (GIS). The landslide susceptibility maps (LSMs) were then classified into four classes; namely, low, medium, high, and very high susceptibility to landslide occurrence, using the natural breaks classification methods in the GIS environment. The very high susceptibility zones produced by these ensemble models cover an area of 630 km2 (WofE& RBF-SVM), 474 km2 (WofE& Linear-SVM), 501km2 (WofE& Polynomial-SVM), and 498 km2 (WofE& Sigmoid-SVM), respectively, of a total area of 3914 km2. The results of our study were validated using the receiver operating characteristic (ROC) curve and quality sum (Qs) methods. The area under the curve (AUC) values of the ensemble WofE& RBF-SVM, WofE & Linear-SVM, WofE & Polynomial-SVM, and WofE & Sigmoid-SVM models are 87%, 90%, 88%, and 85%, respectively, which indicates they are very good models for identifying landslide hazard zones. As per the results of both validation methods, the WofE & Linear-SVM model is more accurate than the other ensemble models. The results obtained from this study using our new ensemble methods can provide proper and significant information to decision-makers and policy planners in the landslide-prone areas of these districts.

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Section: Introductionmentioning

confidence: 99%

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

et al. 2019

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“…Landslides are described as a wide range of processes responsible for the downward and outward movement of slope‐forming material composed of rock, soil, artificial fills, or a combination of all down a slope (Gravina et al ). Globally, more than 80% of landslides occurrence, losses, and damages result from intense and prolonged precipitation, construction involving undercutting of slopes, mining and quarrying, and earthquakes (Banerjee et al ; Huong et al ; Szewrański et al ).…”

Section: Introductionmentioning

confidence: 99%

Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

Nahayo

Ndayisaba

Karamage

et al. 2019

Integr Envir Assess & Manag

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Landslides are among hazards that undermine the social, economic, and environmental well‐being of the vulnerable community. Assessment of landslides vulnerability reveals the damages that could be recorded, estimates the severity of the impact, and increases the preparedness, response, recovery, and mitigation as well. This study aims to estimate landslides vulnerability for the western province of Rwanda. Field survey and secondary data sources identified 96 landslides used to prepare a landslides inventory map. Ten factors—altitude, slope angles, normalized difference vegetation index (NVDI), land use, distance to roads, soil texture, rainfall, lithology, population density, and possession rate of communication tools—were analyzed. The Analytical Hierarchy Process (AHP) model was used to weight and rank the vulnerability conditioning factors. Then the Weighted Linear Combination (WLC) in geographic information system (GIS) spatially estimated landslides vulnerability over the study area. The results indicated the altitude (19.7%), slope angles (16.1%), soil texture (14.3%), lithology (13.5%), and rainfall (12.2%) as the major vulnerability conditioning parameters. The produced landslides vulnerability map is divided into 5 classes: very low, low, moderate, high and very high. The proposed method is validated by using the relative landslides density index (R‐index) method, which revealed that 35.4%, 25%, and 23.9% of past landslides are observed within moderate, high, and very high vulnerability zones, respectively. The consistency of validation indicates good performance of the methodology used and the vulnerability map prepared. The results can be used by policy makers to recognize hazard vulnerability lessening and future planning needs. Integr Environ Assess Manag 2019;00:000–000. © 2019 SETAC

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“…In Teziutlán municipality in Puebla, Mexico, the survey respondents could categorise the natural and human causes of landslides, which indicates a better risk perception and highlighted on reducing vulnerability, improving living standards and enhancing landslide awareness and knowledge at the community level (Hernández-Moreno & Alcántara-Ayala, 2017). In a developed country perspective, for example in Italy, it was found that the landslide-exposed communities were less aware of the disaster risk (Calvello et al, 2016), did not consider landslides as a threat and were less unwilling to participate in prevention activities (Gravina et al, 2017). In all these case studies in an urban hill context, the common recommendations to address landslide disasters were focused on developing landslide early warning systems, initiating communitybased DRR activities (Raška, 2019), integrating land use planning, understanding social vulnerability and increasing communications between communities at risk and government agencies (Klimeš et al, 2019;Antronico et al, 2020).…”

Section: Landslide Vulnerability Of the Rohingya Refugeesmentioning

confidence: 99%

The root causes of landslide vulnerability in Bangladesh

Ahmed

2021

Landslides

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Communities living in the Chittagong Hill Districts (CHD) of Bangladesh recurrently observe landslide disasters during the monsoon season (June–September). CHD is primarily dominated by three distinct groups of hill communities, namely, urbanised hill (Bengali), indigenous tribal and stateless Rohingya refugees. Landslide vulnerability amongst them is complex and varies between physical, social, economic, environmental, institutional and cultural dimensions. This study aims to understand driving forces of landslide disasters in the region by emphasising human factors. Data from the three contrasting communities were collected through participatory workshops, in-depth interviews and fieldwork observation. The participants were local people and landslide experts who were purposefully selected from five case study communities in the CHD. They ranked different socio-economic problems, identified causes of landslides and proposed landslide mitigation action plans. Results suggest that the urbanised Bengali and Rohingya refugee communities are highly vulnerable to landslides. The urbanised hill communities largely deal with poverty, social injustice, lack of planning regulations and illegal hill cutting issues, whereas the Rohingya refugees’ predominant constraints are linked to the ongoing genocide and state-sponsored violence in Myanmar hindering their sustainable repatriation, and their protracted living conditions in Bangladesh. The indigenous tribal communities are comparatively resilient to landslides due to their unique history, traditional knowledge, cultural heritage and lifestyle. Landslides in the CHD should be characterised as socio-natural hazards since the components of landslide disasters are profoundly intertwined with the culture–conflict–corruption nexus.

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Landslide risk perception in Frosinone (Lazio, Central Italy)

Cited by 22 publications

References 19 publications

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

The root causes of landslide vulnerability in Bangladesh

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