0000000000377063
AUTHOR
Fatima Ezahra Sfa
showing 2 related works from this author
A Theoretical Learning Model Combining Stochastic Cellular Automata and Economic Indicators to Simulate Land Use Change
2015
The study of change in land use has been included in territorial planning to inform spatial planners and policy makers of the possible developments they face in order to optimize future management decisions. In this paper the authors present the core of an original learning model coupling stochastic Cellular Automata and economic indicators to simulate the land use change. This model is an important step in building an “environmental virtual laboratory” to explore, explain and forecast land use change.
A generic model of reinforcement learning combined with macroscopic cellular automata to simulate land use change
2019
Better understanding the evolution of land cover is a priority concern in the field of land use change study. This evolution can be the result of interactions between major factors. The study of land use change is included in territorial planning to inform planners and policy makers of possible developments they will face. Land use models are useful for reasonable land use management to optimize future land management decisions. In this paper we present an original theoretical model of reinforcement learning combined with macroscopic cellular automata to simulate land use change.