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

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  • Donatella Caniani
  • Stefania Pascale
  • Francesco Sdao
  • Aurelia Sole

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. Copyright Springer Science+Business Media B.V. 2008

Suggested Citation

  • Donatella Caniani & Stefania Pascale & Francesco Sdao & Aurelia Sole, 2008. "Neural networks and landslide susceptibility: a case study of the urban area of Potenza," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 45(1), pages 55-72, April.
    • Handle: RePEc:spr:nathaz:v:45:y:2008:i:1:p:55-72
    DOI: 10.1007/s11069-007-9169-3
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    Citations

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    Cited by:

    1. Chong Xu & Xiwei Xu & Fuchu Dai & Zhide Wu & Honglin He & Feng Shi & Xiyan Wu & Suning Xu, 2013. "Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 883-900, September.
    2. Shakti Suman & S. Z. Khan & S. K. Das & S. K. Chand, 2016. "Slope stability analysis using artificial intelligence techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 727-748, November.
    3. Maria Kouli & Constantinos Loupasakis & Pantelis Soupios & Filippos Vallianatos, 2010. "Landslide hazard zonation in high risk areas of Rethymno Prefecture, Crete Island, Greece," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 52(3), pages 599-621, March.
    4. Majid Roodposhti & Saeed Rahimi & Mansour Beglou, 2014. "PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(1), pages 77-95, August.
    5. Amin Hosseinpoor Milaghardan & Rahim Ali Abbaspour & Mina Khalesian, 2020. "Evaluation of the effects of uncertainty on the predictions of landslide occurrences using the Shannon entropy theory and Dempster–Shafer theory," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(1), pages 49-67, January.
    6. Maria Karpouza & Konstantinos Chousianitis & George D. Bathrellos & Hariklia D. Skilodimou & George Kaviris & Assimina Antonarakou, 2021. "Hazard zonation mapping of earthquake-induced secondary effects using spatial multi-criteria analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 637-669, October.
    7. Biswajeet Pradhan & Mohammed Abokharima & Mustafa Jebur & Mahyat Tehrany, 2014. "Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 1019-1042, September.
    8. Fatiha Debiche & Mohammed Amin Benbouras & Alexandru-Ionut Petrisor & Lyes Mohamed Baba Ali & Abdelghani Leghouchi, 2024. "Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach," Land, MDPI, vol. 13(6), pages 1-29, June.
    9. Paraskevas Tsangaratos & Andreas Benardos, 2014. "Estimating landslide susceptibility through a artificial neural network classifier," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1489-1516, December.
    10. Ali M. Rajabi & Mahdi Khodaparast & Mostafa Mohammadi, 2022. "Earthquake-induced landslide prediction using back-propagation type artificial neural network: case study in northern Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 679-694, January.
    11. Vahid Nourani & Biswajeet Pradhan & Hamid Ghaffari & Seyed Sharifi, 2014. "Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(1), pages 523-547, March.

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