GIS-based predictive models for regional-scale landslide susceptibility assessment and risk mapping along road corridors

Pellicani, Roberta; Argentiero, Ilenia; Spilotro, Giuseppe

doi:10.1080/19475705.2017.1292411

Cited by 53 publications

(35 citation statements)

References 49 publications

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“…Mean accuracy of ical, hydrological and geological parameters taken into account for these analyses in different contexts by other authors (Budetta, 2004;Jaiswal et al, 2010aJaiswal et al, , b, 2011Quinn et al, 2010;Michoud et al, 2012;Bil et al, 2014Bil et al, , 2017Penna et al, 2014;Ramesh and Anbazhagan, 2015;Pellicani et al, 2017). The reliability of the model is quite fair, as testified by its AUC value (0.73) and by its high value of FP and TN indexes (22.3 and 45.8 %,respectively).…”

Section: Gam Modelmentioning

confidence: 66%

“…Different authors (Budetta, 2004;Jaiswal et al, 2010aJaiswal et al, , b, 2011Quinn et al, 2010;Michoud et al, 2012;Bil et al, 2014Bil et al, , 2017Ramesh and Anbazhagan, 2015;Pellicani et al, 2017;Postance et al, 2017) had already used some of the previously described predictors in different data-driven model aiming to assess roads susceptible to being hit by shallow landslides. Until now, sediment connectivity has not been considered yet.…”

Section: Predictor Variablesmentioning

confidence: 99%

“…In recent years, several data-driven methodologies were built to identify the susceptible sectors of a road network to landslides (Budetta, 2004;Hearn et al, 2008;Jaiswal et al, 2010aJaiswal et al, , b, 2011Quinn et al, 2010;Michoud et al, 2012;Tarolli et al, 2013;Bil et al, 2014Bil et al, , 2017Penna et al, 2014;Ramesh and Anbazhagan, 2015;Tarolli and Sofia, 2016;Winter et al, 2016;Donnini et al, 2017;Pellicani et al, 2017;Postance et al, 2017;Martinovic et al, 2018). These methods are based on quantitative statistical relationships between predisposing factors and a response variable.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

Bordoni

Persichillo

Meisina

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslides cause severe damage to the road network of the hit zone, in terms of both direct (partial or complete destruction of a road or blockages) and indirect (traffic restriction or the cut-off of a certain area) costs. Thus, the identification of the parts of the road network that are more susceptible to landslides is fundamental to reduce the risk to the population potentially exposed and the financial expense caused by the damage. For these reasons, this paper aimed to develop and test a data-driven model for the identification of road sectors that are susceptible to being hit by shallow landslides triggered in slopes upstream from the infrastructure. This model was based on the Generalized Additive Method, where the function relating predictors and response variable is an empirically fitted smooth function that allows fitting the data in the more likely functional form, considering also non-linear relations. This work also analyzed the importance, on the estimation of the susceptibility, of considering or not the sediment connectivity, which influences the path and the travel distance of the materials mobilized by a slope failure until hitting a potential barrier such as a road. The study was carried out in a catchment of northeastern Oltrepò Pavese (northern Italy), where several shallow landslides affected roads in the last 8 years. The most significant explanatory variables were selected by a random partition of the available dataset in two parts (training and test subsets), 100 times according to a bootstrap procedure. These variables (selected 80 times by the bootstrap procedure) were used to build the final susceptibility model, the accuracy of which was estimated through a 100-fold repetition of the holdout method for regression, based on the training and test sets created through the 100 bootstrap model selection. The presented methodology allows the identification, in a robust and reliable way, of the most susceptible road sectors that could be hit by sediments delivered by landslides. The best predictive capability was obtained using a model in which the index of connectivity was also calculated according to a linear relationship, was considered. Most susceptible road traits resulted to be located below steep slopes with a limited height (lower than 50 m), where sediment connectivity is high. Different land use scenarios were considered in order to estimate possible changes in road susceptibility. Land use classes of the study area were characterized by similar connectivity features. As a consequence, variations on the susceptibility of the road network according to different scenarios of distribution of land cover were limited. The results of this research demonstrate the ability of the developed methodology in the assessment of susceptible roads. This could give the managers of infrastructure information about the criticality of the different road traits, thereby allowing attention and economic budgets to be shifted towards the most critical assets, where structural and non-structur...

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Section: Gam Modelmentioning

confidence: 66%

Section: Predictor Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

Bordoni

Persichillo

Meisina

et al. 2018

Nat. Hazards Earth Syst. Sci.

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show abstract

“…(Rupi et al, 2015). Usually, risk estimates related to networks, including road networks, due to landslides are obtained by overlapping hazard and consequence maps 5 (Ferlisi et al, 2012;Pellicani et al, 2017), and therefore, the indirect costs for specific hazard scenarios are not considered.…”

Section: Contextmentioning

confidence: 99%

Estimating the risk related to networks: a methodology and an application on a road network

Hackl

Lam

Heitzler

et al. 2018

Preprint

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Abstract. Networks, such as transportation, water, and power, are critical lifelines to society. Managers plan and execute interventions to guarantee the operational state of their networks under various circumstances, including after the occurrence of (natural) hazard events. Creating an intervention program demands knowing the probable consequences (i.e., risk) of the various hazard events that could occur to be able to mitigate their effects. This paper introduces a methodology to support network managers in the quantification of the risk related to their networks. The method emphasizes the integration of the spatial and 5 temporal attributes of the events that need to be modeled to estimate the risk. This work then demonstrates the usefulness of the methodology through its application to design and implement a risk assessment to estimate the potential impact of flood and mudflow events on a road network located in Switzerland. The example includes the modeling of (i) multiple hazard events, (ii) their physical and functional effects throughout the road network, (iii) the functional interrelationships of the affected objects in the network, (iv) the resulting probable consequences in terms of expected costs of restoration, cost of traffic changes, and 10 duration of network disruption, and (v) the restoration of the network.

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“…In recent years, several data-driven methodologies were built to identify the susceptible sectors of a road network towards landslides (Budetta, 2004;Jaiswal et al, 2010aJaiswal et al, , 2010bJaiswal et al, , 2011Quinn et al, 2010;Michoud et al, 2012;Bil et al, 2014;Ramesh and Anbazhagan, 2015;Pellicani et al, 2017). These methods are based on quantitative statistical relationships between 30 predisposing factors and a response variable, assuming that an event is most likely to occur under similar ground conditions Nat.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

Bordoni¹,

Persichillo²,

Meisina³

et al. 2018

Preprint

View full text Add to dashboard Cite

Abstract. Landslides causes severe damages to the road network of a hit zone, in terms of both direct (partial or complete destruction of a road trait, blockages) and indirect (traffic restriction, cut-off of a certain area) costs. Thus, the identification of the parts of the road network which are more susceptible to landslides is fundamental to reduce the risk to the population potentially exposed and the money expense caused by road damaging. For these reasons, this paper aimed to develop and test a data-driven model based on the Genetic Algorithm Method for the identification of road sectors that are susceptible to be hit 15 by shallow landslides triggered in slopes upstream to the infrastructure. This work also analyzed the importance of considering or not the sediment connectivity on the estimation of the susceptibility. The study was carried out in a catchment of northeastern Oltrepò Pavese (northern Italy), where several shallow landslides affected roads in the last 8 years. The random partition of the dataset used for building the model in two parts (training and test subsets), within a 100-fold bootstrap procedure, allowed to select the most significant explanatory variables, providing a better description of the occurrence and 20 distribution of the road sectors potentially susceptible to damages induced by shallow landslides. The presented methodology allows the identification, in a robust and reliable way, of the most susceptible road sectors that could be hit by sediments delivered by landslides. The best predictive capability was obtained using a model which took into account also the index of connectivity, calculated according to a linear relationship. Most susceptible road traits resulted to be located below steep slopes with a limited height (lower than 50 m), where sediment connectivity is high. Different scenarios of land use were implemented 25 in order to estimate possible changes in road susceptibility. Land use classes of the study area were characterized by similar connectivity features with a consequent loss of variations also on the susceptibility of the road networks according to different scenarios of distribution of land cover. Larger effects on sediment connectivity and, as a consequence on road susceptibility, could be due to modifications in the morphology of the slopes (e.g. drainage system, modification of the slope angle) caused by the abandonment or by the recovery of cultivations. The results of this research demonstrate the ability of the developed 30 methodology in the assessment of susceptible roads. This could give to the managers of an infrastructure information on the Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-457 Manuscript under review for journal Nat. Hazards Earth Syst. Sci. Discussion started: 24 January 2018 c Author(s) 2018. CC BY 4.0 License. 2 criticality of the different road traits, thereby allowing attention and economic budgets to be shifted towards the most critical assets, where structural and non-structural mitigation mea...

show abstract

GIS-based predictive models for regional-scale landslide susceptibility assessment and risk mapping along road corridors

Cited by 53 publications

References 49 publications

Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

Estimating the risk related to networks: a methodology and an application on a road network

Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

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