Abstract
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.
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References
Beale R, Jackson T (1990) Neural computing: an introduction. Hadam Hilger, Bristol
Bishop CM (1996) Neural networks for pattern recognition. Oxford University Press
Brunetti M, Maugeri M, Monti F, Nanni T (2006) Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time. Int J Climatol 26:345–381
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 University
D’Angelo G, Rampone S (2014) Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications. BMC Bioinf 15(Suppl 5):S2
De Martonne E (1926) Aréisme et indice artidite. Comptes Rendus de L’Acad Sci, Paris 182:1395–1398
Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice-Hall, London
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 1–13
Diodato N, Bellocchi G (2008) Drought stress patterns in Italy using agro-climatic indicators. Clim Res 36:53–63
Diodato N, Bellocchi G (2011) Historical perspective of drought response in central-southern Italy. Clim Res 49:189–200
Diouf A, Lambin EF (2001) Monitoring land-cover changes in semi-arid regions: remote sensing and field observations in the Ferlo. Senegal J Arid Environ 48:129–148
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 73
Geist H (2005) The causes and progression of desertification, Ashgate
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. AddisonWesley
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 12–29
Haykin S (2008) Neural networks and learning machines. Prentice Hall, London
Henderson-Sellers A, Irannejad P, McGuffie K (2008) Future desertification and climate change: the need for land-surface system evaluation improvement. Glob Planet Change 64:129–138
Hill J, Stellmes A, Röder UTh, Sommer A S (2008) Mediterranean desertification and land degradation. Mapping related land use change syndromes based on satellite observations. Glob Planet Change 64:146–157
Holm AM, Cridland SW, Roderick ML (2003) The use of time-integrated NOAA NDVI data and rainfall to assess landscape degradation in the arid shrubland of western Australia. Rem Sens Environ 85:145–158
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge
Koza JR (1994) Genetic programming II: automatic discovery of reusable programs. The MIT Press, Cambridge
Le Houerou HN (1984) Rain use efficiency—a unifying concept in arid-land ecology. J Arid Environ 7:213–247
Makkeasorn A, Chang NB, Zhou X (2008) Short-term streamflow forecasting with global climate change implications—a comparative study between genetic programming and neural network model. J Hydrol 352:336–354
Nicholson SE, Tucker CJ, Ba MB (1998) Desertification, drought and surface vegetation: an example from the West African Sahel. Bull Am Meterol Soc 79:815–829
O’Connor TG, Haines LM, Snyman HA (2001) Influence of precipitation and species composition on phytomass of semi-arid African grassland. J Ecol 89:850–861
Prince SD, Brown de Colstoun E, Kravitz LL (1998) Evidence from rain-use efficiencies does not indicate extensive Sahelian desertification. Glob Change Biol 4:359–374
Rampone S (2013) Three-and-six-month-before forecast of water resources in a karst aquifer in the Terminio massif (Southern Italy). Appl Soft Comput 13(10):4077–4086
Rampone S, Russo C (2012) A fuzzified BRAIN algorithm for learning DNF from incomplete data. Electron J Appl Stat Anal (EJASA) 5(2):256–270
Rampone S, Valente A (2012) Neural network aided evaluation of landslide susceptibility in southern Italy. Int J Mod Phys C 23(01):1250002
Rampone S, Valente A (2017) Prediction of seasonal temperature using soft computing techniques: application in benevento (Southern Italy) area. J Ambient Intell Humaniz Comput 8(1):147–154
Salvati L, Ceccarelli T, Brunetti A (2005) Valutazione del rischio di desertificazione in Italia: primi risultati. It J Agromet 9:124–125
Schmidt M, Lipson H (2009) Distilling free-form natural laws from experimental data. Science 324(Issue 5923):81–85
Shiri J, Kişi O, Landeras G, Lopez JJ, Nazemi AH, Stuyt LCPM (2012) Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). J Hydrol 414–415:302–316
Sivapragasam C, Maheswaran R, Venkatesh V (2007) Genetic programming approach for flood routing in natural channels. Hydrol Process 22(5):623–628
Stanislawska K, Krawiec K, Kundzewicz ZW (2012) Modelling global temperature changes with genetic programming. Comput Math Appl 64:3717–3728
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 York
UNEP (United Nations Environmental Program) (1992) World Atlas of Desertification (editorial commentary by N. Middleton and D.S.G. Thomas). Arnold, London
United Nations Conference on Desertification (UNCOD) (1977) Desertification: Its Causes and Consequences. Pergamon Press, Oxford, p 448
Veron SR, Paruelo JM, Oesterheld M (2006) Assessing desertification. J Arid Environ 66:751–763
Verstraete MM, Brink AB, Scholes RJ, Beniston M, Stafford Smith M (2008) Climate change and desertification: where do we stand, where should we go? Glob. Plan Change 64:105–110
Vogt JV, Safriel U, Von Maltitz G, Sokona Y, Zougmore R, Bastin G, Hill J (2011) Monitoring and assessment of land degradation and desertification: towards new conceptual and integrated approaches. Land Degrad Dev 22:150–165
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 15–44
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The authors are grateful to L. Rampone for the careful reading of the paper.
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In collaboration with the Environmental Data Processing Course Group of Università del Sannio: Gabriele Lepore, Antonello Aufiero, Ester Manganiello, Vincenzo Cuoco, Gianluca Iuliano, Cosimo Serino, Alessandro Tedesco.
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Rampone, S., Valente, A. Assessment of desertification vulnerability using soft computing methods. J Ambient Intell Human Comput 10, 701–707 (2019). https://doi.org/10.1007/s12652-018-0720-8
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DOI: https://doi.org/10.1007/s12652-018-0720-8