The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models

Bordoni, Massimiliano; Galanti, Yuri; Bartelletti, Carlotta; Persichillo, Maria Giuseppina; Barsanti, Michele; Giannecchini, Roberto; Avanzi, G. D’Amato; Cevasco, Andrea; Brandolini, Pierluigi; Galvé, Jorge Pedro; Meisina, Claudia

doi:10.1016/j.catena.2020.104630

Cited by 67 publications

(30 citation statements)

References 73 publications

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“…In any case, a significance of the extreme event cannot be excluded, which is widely documented in the case of surface gravitational phenomena [39,40]. Among the discriminating and statistically significant parameters for the production of landslide movements, there are the slope gradient, which from 5 to 30 • shows an excellent correlation, the agricultural terrain and the geological formation MUS, according to the relevant scientific literature [9,41,42]. The validation procedure allowed, the reliability of the model to be assessed at about 70%, in line with many other studies that used the same or different calculation methods [15,43].…”

Section: Discussionmentioning

confidence: 99%

Statistical Analysis of Landslide Susceptibility, Macerata Province (Central Italy)

2021

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Every year, institutions spend a large amount of resources to solve emergencies generated by hydrogeological instability. The identification of areas potentially subject to hydrogeological risks could allow for more effective prevention. Therefore, the main aim of this research was to assess the susceptibility of territories where no instability phenomena have ever been detected. In order to obtain this type of result, statistical assessments of the problem cannot be ignored. In this case, it was chosen to analyse the susceptibility to landslide using a flexible method that is attracting great interest in the international scientific community, namely the Weight of Evidence (WoE). This model-building procedure, for calculating landslide susceptibility, used Geographic Information Systems (GIS) software by means of mathematical operations between rasters and took into account parameters such as geology, acclivity, land use, average annual precipitation and extreme precipitation events. Thus, this innovative research links landslide susceptibility with triggering factors such as extreme precipitation. The resulting map showed a low weight of precipitation in identifying the areas most susceptible to landslides, although all the parameters included contributed to a more accurate estimate, which is necessary to preserve human life, buildings, heritage and any productive activity.

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Section: Discussionmentioning

confidence: 99%

Statistical Analysis of Landslide Susceptibility, Macerata Province (Central Italy)

2021

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“…As reported by Zhang et al [72], the slope angle is a key factor for slope stability and is one of the most commonly used parameters in LSM studies [73][74][75]. The average slope of the study area, where the slope ranges from 0 • to 76.95 • , is 28.38 • .…”

Section: Landslide-conditioning Factorsmentioning

confidence: 95%

Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey

Akıncı

Kılıçoğlu

Doğan

2020

IJGI

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Natural disasters such as landslides often occur in the Eastern Black Sea region of Turkey owing to its geological, topographical, and climatic characteristics. Landslide events occur nearly every year in the Arhavi, Hopa, and Kemalpaşa districts located on the Black Sea coast in the Artvin province. In this study, the landslide susceptibility map of the Arhavi, Hopa, and Kemalpaşa districts was produced using the random forest (RF) model, which is widely used in the literature and yields more accurate results compared with other machine learning techniques. A total of 10 landslide-conditioning factors were considered for the susceptibility analysis, i.e., lithology, land cover, slope, aspect, elevation, curvature, topographic wetness index, and distances from faults, drainage networks, and roads. Furthermore, 70% of the landslides on the landslide inventory map were used for training, and the remaining 30% were used for validation. The RF-based model was validated using the area under the receiver operating characteristic (ROC) curve. Evaluation results indicated that the success and prediction rates of the model were 98.3% and 97.7%, respectively. Moreover, it was determined that incorrect land-use decisions, such as transforming forest areas into tea and hazelnut cultivation areas, induce the occurrence of landslides.

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“…As debris flows propagated into the drainage network, they were not included in this analysis. In fact, in this case, the deposits were swept away along the channels and uncertainties occurred in identifying the boundary between debris flows and debris flood deposits [14].…”

Section: Landslide Detachment Transit and Runout Susceptibility (Ldtmentioning

confidence: 99%

“…Shallow landslides usually occur on steep soil-mantled slopes [1][2][3][4][5][6] as a consequence of heavy or intense rainfalls and often may evolve into potentially catastrophic flow-like movements. Slope failures are very common in hilly and mountainous areas due to the combination of several controlling factors such as slope steepness [7][8][9][10], intense or prolonged rainfalls [2,[11][12][13][14], land-use changes [15][16][17][18] and wildfires [19,20]. The consequences of shallow landslides and flow-like phenomena are typically more dangerous in inhabited centers located at the foot of slopes, where the hydrographic network is well developed.…”

Section: Introductionmentioning

confidence: 99%

Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

Napoli

Martire

Bausilio

et al. 2021

Water

Self Cite

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Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed.

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The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models

Cited by 67 publications

References 73 publications

Statistical Analysis of Landslide Susceptibility, Macerata Province (Central Italy)

Statistical Analysis of Landslide Susceptibility, Macerata Province (Central Italy)

Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey

Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

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