Artificial neural network for multifunctional areas

Environmental Monitoring and Assessment

Volumn 188, Issue 1, 2016, Pages 1-11

  Riccioli, Francesco ^a   El Asmar, Toufic ^b   El Asmar, Jean Pierre ^c   Fagarazzi, Claudio ^a   Casini, Leonardo ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

^b FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS   (Italy)

^c NOTRE DAME UNIVERSITY   (Lebanon)

Author keywords

Artificial neural network; GIS; Land use change; Territorial planning

Indexed keywords

ECONOMICS; GEOGRAPHIC INFORMATION SYSTEMS; LAND USE; NEURAL NETWORKS; SUSTAINABLE DEVELOPMENT; URBAN PLANNING;

ARTIFICIAL NEURAL NETWORK MODELS; ENVIRONMENTAL VALUES; FLORENCE; INHABITED AREAS; LAND-USE CHANGE; SUSTAINABLE GROWTH; TERRITORIAL PLANNING; URBAN AREAS;

URBAN GROWTH;

ARTIFICIAL NEURAL NETWORK; GIS; LAND USE CHANGE; LAND USE PLANNING; SUSTAINABLE DEVELOPMENT; TERRITORIAL PLANNING;

ARTIFICIAL NEURAL NETWORK; ITALY; LANDSCAPE; URBAN AREA; ENVIRONMENT; ENVIRONMENTAL MONITORING; ENVIRONMENTAL PROTECTION; GEOGRAPHIC INFORMATION SYSTEM; HUMAN; PROCEDURES; URBANIZATION;

CHIANTI; ITALY; TUSCANY;

CONSERVATION OF NATURAL RESOURCES; ENVIRONMENT; ENVIRONMENTAL MONITORING; GEOGRAPHIC INFORMATION SYSTEMS; HUMANS; ITALY; NEURAL NETWORKS (COMPUTER); URBANIZATION;

EID: 84952906322     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-015-5072-7     Document Type: Article

References (39)

1. Aaviksoo, K. (1995). Simulating vegetation dynamics and land-use in a mire landscape using a Markov model. Landscape and Urban Planning, 31, 129–142.
2. Agarwal, C., Green, G. M., Grove, J. P., Evans, T. P., & Schweik, C. M. (2002). A review and assessment of land use change models (CIPES collaborative report series No.1).
3. Alsharif, A. A., & Pradhan, B. (2014). Urban sprawl analysis of Tripoli Metropolitan City (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing, 42(1), 149–163. Springer.
4. Baker, W. L. (1989). A review of models of landscape change. Landscape Ecology, 2(2), 111–133.
5. Basse, R., Omrani, H., Charif, O., Gerber, P., & Bòdis, K. (2014). Land use changes modelling using advanced methods: cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Applied Geography, 53, 160–171. Elsevier.
6. Bernetti, I., & Marinelli, N. (2009). Cambiamenti in atto nel paesaggio agrario toscano. Possibili scenari evolutivi. I Georgofili, 4, 633–677.
7. Bonham-Carter, G.F. (1994). Geographic information systems for geoscientists: modelling with GIS. Ontario, Canada: Pergamon.
8. Coquillard, P., & Hill, D. R. C. (1997). Modélisation et simulation d’écosystèmes. Paris: MASSON.
9. Costanza, R., & Ruth, M. (1998). Using dynamic modeling to scope environmental problems and built consensus. Environmental Management, 22(2), 183–195.
10. Eastman, J. R. (2006). IDRISI Andes, tutorial. MA, USA: Clark Labs, Clark University.
11. Eastman, J. R., & Toledano, J. (2000). Markov chain and cellular automata approaches to land cover change modeling. In Workshop presentation (invited), 4th International Conference on Integrating GIS and Environmental Modeling, Banff, Alberta, Sept. 2–8, 2000.
12. EEA. (2006). Land accounts for Europe 1990–2000. EEA report No 11/2006.
13. Engelen, G., White, R., Van Der Meulen, M., & Hahn, B. (2002). Sustainable developments of islands: a policy support framework for the integrated assessment of socio-economic and environmental development. In H.-H. M. Hsiao, C.-H. Liu, & H.-M. Tsai (Eds.), Sustainable development for island societies: Taiwan and the World (pp. 251–287). Taipei, Taiwan: Asia-Pacific Research Program, Academia Sinica and SARCS Secretariat.
14. Grekousis, G., Manetos, P., & Photis, Y. N. (2013). Modeling urban evolution using neural networks, fuzzy logic and GIS: the case of the Athens metropolitan area. Cities, 30, 193–203. Elsevier.
15. ISTAT. (2001). V Censimento Generale dell’Agricoltura. Roma. http://censagr.istat.it/dati.htm. Accessed 24 Sept 2002.
16. Lambin, E. (1997). Modelling and monitoring land-cover change processes in tropical regions. Progress in Physical Geography, 21(3), 375–393.
17. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.
18. Logofet, D. O., & Lesnaya, E. V. (2000). The mathematics of Markov models: what Markov chains can really predict in forest successions. Ecological Modeling, 126, 285–298.
19. Lombardo, S., Pecori, S., & Petri, M. (2005). Investigating territorial dynamic using decision tree (Atti del convegno Cupum).
20. Lopez, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land cover and land use change in the urban fringe. A case in Morelia city, Mexico. Landscape and Urban Planning, 55, 271–282. Elsevier.
21. Mahboob, M. A., Atif, I., & Iqbal, J. (2015). Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, 34(3), 179–188. doi:10.3923/std.2015.179.188.
22. Malczewski, J. (1999). Gis and multicriteria decision analysis. Toronto, Ontario, Canada: John Winley & Sons, Inc.
23. Malczewski, J. (2004). Gis–based land-use suitability analysis: a critical overview. Volume 62, Issue 1, pp 3–65, Toronto: Elsevier.
24. Martinuzzi, S., Gould, W., & Gonzales, O. (2007). Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data. Landscape and Urban Planning, 79, 288–297. Elsevier.
25. Matheron, G. (1978). Estimer et choisir (Cahiers du centre de morphologie mathématique de Fontainebleau, Fasc.7, Ecole de Mines de Paris).
26. Matheron, G. (1989). Estimating and choosing – an essay on probability in practice. Berlin: Springer.
27. Mazzocchi, C., Corsi, S., Sali, G. (2014). Agricultural land consumption in periurban areas: a methodological approach for risk assessment using artificial neural networks and spatial correlation in Northern Italy. Applied Spatial Analysis. doi:10.1007/s12061-015-9168-9. Springer.
28. O’Sullivan, D., & Unwin, D. J. (2003). Geographic information analysis. Hoboken: Wiley.
29. Park, S., Jeon, S., Kim, S., & Choi, C. (2011). Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landscape and Urban Planning, 99, 104–114. Elsevier.
30. Pijanowski, B.B., Brown, D.G., Shellito, B.A., Manik, G.A. (2002). Using neural networks and GIS to forecast land use changes: a Land Transformation Model. Computers, Environment and Urban Systems, 26(6), 553–575. Elsevier.
31. Prieler, S. (2005). Built-up and associated land area increases in Europe (WP 3. Environmental Evaluation, International Institute for Applied Systems Analysis (IIASA), Land Use Change and Agriculture Project).
32. Riccioli, F. (2004). Il paesaggio nelle valutazioni di impatto ambientale. Atti del XXXIV Incontro di Studio CeSET “Le grandi infrastrutture: approcci di ordine giuridico, economico ed estimativo” Firenze 15–16 ottobre 2004. University press: Florence, Italy
33. Riccioli, F. (2007). La funzione socio-economica delle attività vitivinicole letta attraverso il Piano di Sviluppo Rurale toscano. In F. A. Milano (Ed.), Menghini S. (a cura di) “Il ruolo del settore vitivinicolo nei processi di sviluppo sostenibile” (pp. 52–92).
34. Riccioli, F. (2009). Una proposta metodologica per lo studio della multifunzionalità: l’analisi MultiCriteriale Geografica (Economia e diritto agroambientale n.3, pp. 99–118). Firenze: University Press.
35. Steyaert, L. T. (1993). A perspective on the state of environmental simulation modeling (Environmental modeling with GIS, Pag. 16–30). New York: Oxford University Press.
36. Tang, Z., Engel, B. A., Pijanowski, B. C., & Lim, K. J. (2005). Forecasting land use change and its environmental impact at a watershed scale. Elsevier, Journal of Environmental Management, 76, 35–45.
37. Triantakonstantis, D., & Stathakis, D. (2015). Urban growth prediction in Athens, Greece, using Artificial Neural Networks. International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering, 9(3), 2015.
38. Verburg, P. H., Soepboer, W., Limpiada, R., Espaldon, M. V. O., Sharifa, M. A., & Veldkamp, A. (2002). Modelling the spatial dynamics of regional land use: the CLUE-S model. Environmental Management, 30, 391–405.
39. Xia, L., & Gar-On Yeh, A. (2002). Neural network based cellular automata for simulating multiple land use changes using GIS. International Journal Geographical Information Science, 16(4), 323–343. Taylor & Francis.