Landslide Susceptibility Mapping Using Artificial Neural Network in the Urban Area of Senise and San Costantino Albanese (Basilicata, Southern Italy) | SpringerLink
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Landslide Susceptibility Mapping Using Artificial Neural Network in the Urban Area of Senise and San Costantino Albanese (Basilicata, Southern Italy)

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Computational Science and Its Applications – ICCSA 2013 (ICCSA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7974))

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Abstract

Landslides are significant natural hazards in many areas of the world. Mapping the areas that are susceptible to landslides is essential for a wise territorial approach and should become a standard tool to support land-use management. A landslide susceptibility map indicates landslide-prone areas by considering the predisposing factors of slope failures in the past. In the presented work, we evaluate the landslide susceptibility of the urban area of Senise and San Costantino Albanese towns (Basilicata, southern Italy) using an Artificial Neural Network (ANN). In order, this method has required the definition of appropriate thematic layers, which parameterize the area under study. To evaluate and validate landslide susceptibility, the landslides have been randomly divided into two groups, each representing the 50% of the total area subject to instability. The results of this research show that most of the investigated area is characterized by a high landslide hazard.

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Authors and Affiliations

  1. School of Engineering, University of Basilicata, 85100, Potenza, PZ, Italy

    Stefania Pascale, Serena Parisi, Annagrazia Mancini, Aurelia Sole, Beniamino Murgante & Francesco Sdao

  2. Department of Science, University of Basilicata, 85100, Potenza, PZ, Italy

    Marcello Schiattarella

  3. CNR - Institute for Agriculture and Forest Systems in the Mediterranean (ISAFOM), Via Cavour 4/6, 87036, Rende, CS, Italy

    Massimo Conforti

Authors
  1. Stefania Pascale
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  2. Serena Parisi
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  3. Annagrazia Mancini
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  4. Marcello Schiattarella
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  5. Massimo Conforti
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  6. Aurelia Sole
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  7. Beniamino Murgante
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  8. Francesco Sdao
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Editor information

Editors and Affiliations

  1. L-I.S.U.T. - D.A.P.I.t. Facoltà Ingegneria, Università degli Studi della Basilicata, Viale dell’Ateneo Lucano, 10, 85100, Potenza, Italy

    Beniamino Murgante

  2. Canaanland OTA, Covenant University, Nigeria

    Sanjay Misra

  3. Partimento di Scienze e Tecnologie per LAgricoltura, le Foreste, la Natura e lEnergia, Università degli Studi della Tuscia, Via S. Camillo de Lellis, snc, 01100, Viterbo, Italy

    Maurizio Carlini

  4. Dipartimento di Scienze dell’Ingegneria Civile e dell’Architecttura, Politecnico di Bari, Via Orabona, 4, 70125, Bari, Italy

    Carmelo M. Torre

  5. Quarter 6, Linh Trung, Thu Duc, International University VNU-HCM, Ho Chi Minh City, Vietnam

    Hong-Quang Nguyen

  6. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

  8. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

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Pascale, S. et al. (2013). Landslide Susceptibility Mapping Using Artificial Neural Network in the Urban Area of Senise and San Costantino Albanese (Basilicata, Southern Italy). In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39649-6_34

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  • DOI: https://doi.org/10.1007/978-3-642-39649-6_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39648-9

  • Online ISBN: 978-3-642-39649-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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