Neural networks and landslide susceptibility: a case study of the urban area of Potenza |
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| Authors: | Donatella Caniani Stefania Pascale Francesco Sdao Aurelia Sole |
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| Institution: | (1) Department of Engineering and Physics of the Environment, University of Basilicata, Potenza, Italy;(2) Dipartimento di Strutture, Geotecnica, Geologia Applicata, University of Basilicata, Viale dell’ Ateneo Lucano, 10, Potenza, 85100, Italy |
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| 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. |
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| Keywords: | Landslide susceptibility map Artificial Neural Network Municipal area of Potenza (Basilicata Southern Italy) |
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