Artificial neural network for multifunctional areas.

Journal: Environmental monitoring and assessment
Published Date:

Abstract

The issues related to the appropriate planning of the territory are particularly pronounced in highly inhabited areas (urban areas), where in addition to protecting the environment, it is important to consider an anthropogenic (urban) development placed in the context of sustainable growth. This work aims at mathematically simulating the changes in the land use, by implementing an artificial neural network (ANN) model. More specifically, it will analyze how the increase of urban areas will develop and whether this development would impact on areas with particular socioeconomic and environmental value, defined as multifunctional areas. The simulation is applied to the Chianti Area, located in the province of Florence, in Italy. Chianti is an area with a unique landscape, and its territorial planning requires a careful examination of the territory in which it is inserted.

Authors

  • Francesco Riccioli
    Department of Management of Agricultural, Food and Forestry Systems, University of Florence, Florence, Italy. francesco.riccioli@unifi.it.
  • Toufic El Asmar
    Food and Agriculture Organization of the United Nations (FAO) - Plant Production and Protection Division, Rome, Italy. Toufic.Elasmar@fao.org.
  • Jean-Pierre El Asmar
    Notre Dame University - Louaize - Faculty of Architecture Art and Design, Zouk Mosbeh, Lebanon. jasmar@ndu.edu.lb.
  • Claudio Fagarazzi
    Department of Management of Agricultural, Food and Forestry Systems, University of Florence, Florence, Italy. claudio.fagarazzi@unifi.it.
  • Leonardo Casini
    Department of Management of Agricultural, Food and Forestry Systems, University of Florence, Florence, Italy. leonardo.casini@unifi.it.