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

摘要

Identifying and evaluating the driving forces that are behind land use and land cover changes remains one of the most difficult exercises that geographers and environmental scientists must continually address. The difficulty emerges from the fact that in land use and land cover systems, multiple actions and interactions between different factors (e.g., economic, political, environmental, biophysical, institutional, and cultural) come into play and make it difficult to understand how the processes behind the changes function. Using advanced methods, such as Cellular Automata (CA) and Artificial Neural Networks (ANNs), the results highlight that these tools are adequate in formalising knowledge regarding land use systems in cross-border regions. Moreover, because modelling land use changes using big data is gaining increasing popularity, ANN techniques could contribute to improving the calibration of cellular automata-based land use models