Literature DB >> 26718948

Artificial neural network for multifunctional areas.

Francesco Riccioli1, Toufic El Asmar2, Jean-Pierre El Asmar3, Claudio Fagarazzi4, Leonardo Casini5.   

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.

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

Mesh:

Year:  2015        PMID: 26718948     DOI: 10.1007/s10661-015-5072-7

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  4 in total

1.  Modeling the spatial dynamics of regional land use: the CLUE-S model.

Authors:  Peter H Verburg; Welmoed Soepboer; A Veldkamp; Ramil Limpiada; Victoria Espaldon; Sharifah S A Mastura
Journal:  Environ Manage       Date:  2002-09       Impact factor: 3.266

2.  Forecasting land use change and its environmental impact at a watershed scale.

Authors:  Z Tang; B A Engel; B C Pijanowski; K J Lim
Journal:  J Environ Manage       Date:  2005-07       Impact factor: 6.789

3.  Using Dynamic Modeling to Scope Environmental Problems and Build Consensus

Authors: 
Journal:  Environ Manage       Date:  1998-03       Impact factor: 3.266

4.  The measurement of observer agreement for categorical data.

Authors:  J R Landis; G G Koch
Journal:  Biometrics       Date:  1977-03       Impact factor: 2.571

  4 in total
  2 in total

1.  Scenario prediction of emerging coastal city using CA modeling under different environmental conditions: a case study of Lingang New City, China.

Authors:  Yongjiu Feng; Yan Liu
Journal:  Environ Monit Assess       Date:  2016-08-31       Impact factor: 2.513

2.  Quantitative Morphology of Polder Landscape Based on SOM Identification Model: Case Study of Typical Polders in the South of Yangtze River.

Authors:  Zhe Li; XinYi Lu; Xiao Han; LiYa Wang; XiaoJian Tang; XiaoShan Lin
Journal:  Comput Intell Neurosci       Date:  2022-05-29
  2 in total

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