Strategies investigation in using artificial neural network for landslide susceptibility mapping: application to a Sicilian catchment

Risultato della ricerca: Articlepeer review

19 Citazioni (Scopus)

Abstract

Susceptibility assessment of areas prone to landsliding remains one of the most useful approaches in landslide hazard analysis. The key point of such analysis is the correlation between the physical phenomenon and its triggering factors based on past observations. Many methods have been developed in the scientific literature to capture and model this correlation, usually within a geographic information system (GIS) framework. Among these, the use of neural networks, in particular the multi-layer perceptron (MLP) networks, has provided successful results. A successful application of the MLP method to a basin area requires the definition of different model strategies, such as the sample selection for the training phase or the design of the network structure. The present study investigates the effects of these strategies on the development of landslide susceptibility maps by applying different model configurations to a small basin located in northeastern Sicily (Italy), where a number of historical slope failure events have been documented over the years. Model performances and their comparison are evaluated using specific metrics.
Lingua originaleEnglish
pagine (da-a)502-515
Numero di pagine14
RivistaJournal of Hydroinformatics
Volume16
Stato di pubblicazionePublished - 2014

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Water Science and Technology
  • Geotechnical Engineering and Engineering Geology
  • Atmospheric Science

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