Neural networks and landslide susceptibility: a case study of the urban area of Potenza

Abstract

For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful information regarding possible future phenomena. Such maps represent a fundamental step in the drawing up of adequate measures of landslide hazard mitigation. However, for a complete estimation of landslide hazard, meant as the degree of probability that a landslide occurs in a given area, within a given space of time, detailed and uniformly distributed data regarding their incidence and causes are required. This information, while obtainable through laborious historical research, is usually partial, incomplete and uneven, and hence, unsatisfactory for zoning on a regional scale. In order to carry this out effectively, the utilization of spatial estimation of the relative levels of landslide hazard in the various areas was considered opportune. These areas were classified according to their levels of proneness to landslide activity without taking recurrence periods into account. Various techniques were developed in order to obtain upheaval numerical estimates. The method used in this study, which was applied in the area of Potenza, is based on techniques derived from artificial intelligence (Artificial Neural Network - ANN). This method requires the definition of appropriate thematic layers, which parameterize the area under study. These are recognized by means of specific analyses in a functional relationship to the event itself. The parameters adopted are: slope gradient, slope aspect, topographical index, topographical shape, elevation, land use and lithology

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

“…Basilicata Region (Southern Italy) is, for geological, geomorphological, climatic and seismic reasons, one of the most landslide prone areas of the Mediterranean basin regions, which is characterized by landslides of different types, in which mechanisms of evolution and processes of erosion are intensity selective (Caniani et al, 2008;Pascale et al, 2010Pascale et al, , 2012Sdao, 1996, 1998;Gullà and Sdao, 2001;De Bari et al, 2011). In some areas of Basilicata, the landslides are so intense and widespread that they sometimes generate serious damage to people and properties.…”

Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China

“…In fact, the prediction samples usually cannot pass the hypothesis test in the assessment process. Some other approaches have also been developed, according to the methodologies of decision tree (DT) [22], genetic algorithm (GA) [23], artificial neural network (ANN) [15,[24][25][26][27][28][29], and support vector machine (SVM) [30][31][32][33]. These objective statistical methods were used for evaluating the relationships between various influencing factors and landslide inventories.…”

Mass movements inventory map of the Rubbio stream catchment (Basilicata – South Italy)

“…Catenacci, 1992;Catani, Casagli, Ermini, Righini, & Menduni, 2005;Conforti, Robustelli, Muto, & Critelli, 2012a;Conforti, Pascale, Robustelli, & Sdao, 2014a;Conforti, Muto, Rago, & Critelli, 2014b;Guzzetti, 2000;Luca, Robustelli, Conforti, & Fabbricatore, 2011). In the Basilicata region (southern Italy), mass movements cause considerable damage to human activity and property every year (Bentivenga, Palladino, & Caputi, 2012;Caniani, Pascale, Sdao, & Sole, 2008;Conforti, Pascale, Pastore, Pepe, Sdao & Sole, 2012b;Conforti, Pascale, Pepe, Sdao, & Sole, 2013;Dal Sasso et al, 2014;Pascale, Sdao, & Sole, 2010;Pascale et al, 2013;Sdao & Simeone, 2007). Their occurrence is controlled both by a series of predisposing factors (e.g.…”

“…Their occurrence is controlled both by a series of predisposing factors (e.g. geological, geomorphological, climatic, hydro-geological) and triggering factors (seismicity, heavy rainfall, human activities); the different combinations of these geo-environmental features produce a wide variety of mass movements in terms of typology, kinematic mechanisms, evolution and dimensions (Caniani et al, 2008;Conforti, Pascale, Pastore, Pepe, Sdao & Sole, 2012b;Conforti, Pascale, Pepe et al, 2013;De Bari et al, 2011;Pascale et al, 2013).…”