Assessment of desertification vulnerability using soft computing methods
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In this work Artificial Neural Networks and Genetic Programming are applied in order to assess the desertification status, a kind of land degradation, of an area, from meteorological and land use data. The approach has been tested in the Sannio (central Italy) region. Both the used soft computing methods show low error rates, and the Genetic Programming offers the advantage of an explicit representation of the factors that favour or delay the desertification. This methodology allows preventive actions to face the upcoming desertification.
KeywordsDesertification Soft computing Artificial neural networks Genetic programming Central Italy
The authors are grateful to L. Rampone for the careful reading of the paper.
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
- Bishop CM (1996) Neural networks for pattern recognition. Oxford University PressGoogle Scholar
- Cramer NL (1985) A representation for the Adaptive generation of simple sequential programs. In: Proceedings of an international conference on genetic algorithms and the applications, Grefenstette, John J. (ed.), Carnegie Mellon UniversityGoogle Scholar
- De Martonne E (1926) Aréisme et indice artidite. Comptes Rendus de L’Acad Sci, Paris 182:1395–1398Google Scholar
- Di Lisio A, Lo Curzio S, Russo F, Sisto M (2009) Rappresentazione degli indici climatici in ambiente GIS per la caratterizzazione paesaggistica dell’Appennino Sannita (Campania). Atti 13a Conferenza Nazionale ASITA, 1–4 dicembre 2009, Bari, pp 1–13Google Scholar
- FAO/UNEP (1984) Provisional Methodology for Assessment and Mapping of Desertification. Food and Agriculture Organization of the United Nations, United Nations Environmental Programme, Rome, p 73Google Scholar
- Geist H (2005) The causes and progression of desertification, AshgateGoogle Scholar
- Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. AddisonWesleyGoogle Scholar
- Gringof IG, Mersha E (2006) Assessment of desertification, drought and other extreme meteorological events. In: Gathara ST, Gringof LG, Mersha E, Sinha Ray KC, Spasov P (eds) Impacts of desertification and drought and other extreme meteorological events. World Meteorological Organization, Geneva, pp 12–29Google Scholar
- Haykin S (2008) Neural networks and learning machines. Prentice Hall, LondonGoogle Scholar
- Le Houerou HN (1984) Rain use efficiency—a unifying concept in arid-land ecology. J Arid Environ 7:213–247Google Scholar
- Salvati L, Ceccarelli T, Brunetti A (2005) Valutazione del rischio di desertificazione in Italia: primi risultati. It J Agromet 9:124–125Google Scholar
- UN (United Nations) (1994) United Nations Convention to Combat Desertification in Countries Experiencing Serious Drought and/or Desertification, Particularly in Africa. Document A/AC. 241/27, 12. 09. 1994 with Annexes. United Nations, New YorkGoogle Scholar
- UNEP (United Nations Environmental Program) (1992) World Atlas of Desertification (editorial commentary by N. Middleton and D.S.G. Thomas). Arnold, LondonGoogle Scholar
- United Nations Conference on Desertification (UNCOD) (1977) Desertification: Its Causes and Consequences. Pergamon Press, Oxford, p 448Google Scholar
- Wigley TML (1992) Future climate of the Mediterranean basin with particular emphasis changes in precipitation. In: Jeftic L, Millman JD, Sestini G (eds) Climate change and the Mediterranean. Edward Arnold, London, pp 15–44Google Scholar