A Hybrid CA-ANN-Fuzzy Model for Simulating Coastal Changing Patterns

  • Jorge Rocha
  • Francisco GutierresEmail author
  • Pedro Gomes
  • Ana Cláudia Teodoro
Part of the Coastal Research Library book series (COASTALRL, volume 24)


Shoreline erosion is a problem that causes major concerns to coastal cities worldwide. About 70% of the world’s sandy beaches retreated at a rate of 0.5–1.0 m.year−1. Therefore, the protection against beach loss and appropriate land management along the shoreline are critical issues that need to be addressed. The modelling and simulation of dynamic and complex systems, such as coastal areas, are important for the definition of an innovative planning and management strategy. To explore sandy beaches threatened by shoreline retreat, this works aims to develop a geosimulation hybrid model. The geosimulation (geocomputation) is an emergent field of analysis embracing heuristic search, artificial neural networks and cellular automata, among others. In this chapter we present a method to simulate both the coast line and the land use/cover evolution in a developed costal area reality, by coupling cellular automata (CA) and multi-layer perceptron (MLP) artificial neural network (ANN) with fuzzy set theory (CA–ANN-Fuzzy) in a GIS environment. Such alterations simulation solely by means of cellular automata isn’t suitable, because these models, in its more conventional structure, comprise limitations in the space parameters and transition rules. In this work a neural network is used to calibrate the importance degree that each prediction variable (probability) has in the geographic constraints (weights), i.e. considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes, while fuzzy set theory captures the uncertainty associated with transition rules. The proposed method predict high shoreline drawbacks in only 14 years, mainly at North (40 meters) and West (20 meters). The model has an overall accuracy of 86% (14% of error in 60 years).


Simulation Cellular automata Artificial neural networks Fuzzy logic 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jorge Rocha
    • 1
  • Francisco Gutierres
    • 2
    Email author
  • Pedro Gomes
    • 3
  • Ana Cláudia Teodoro
    • 4
  1. 1.Institute of Geography and Spatial PlanningUniversidade de LisboaLisboaPortugal
  2. 2.Big Data Analytics UnitEurecat – Technology Centre of CataloniaBarcelonaSpain
  3. 3.Department of Environment and AgricultureNational Statistics InstituteLisbonPortugal
  4. 4.Earth Sciences Institute (ICT) and Department of Geosciences, Environment and Land Planning, Faculty of SciencesUniversity of PortoPortoPortugal

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