Neural-network-based cellular automata for simulating multiple land use changes using GIS

TLDR

Simulating multiple land use changes with cellular automata is challenging due to the need for numerous spatial variables, parameters, and the difficulty of defining transition rules and model structures. The study introduces a neural‑network‑based cellular automata approach, using GIS, to simulate the evolution of multiple land uses by computing conversion probabilities for competing land uses. The method iteratively applies a three‑layer neural network, whose parameters are automatically learned from GIS‑derived site attributes, to compute conversion probabilities and update spatial variables at each loop, thereby simulating gradual land‑use changes. The model was successfully applied to simulate multiple land‑use changes in a rapidly growing region of southern China.

Abstract

This paper presents a new method to simulate the evolution of multiple land uses based on the integration of neural networks and cellular automata using GIS. Simulation of multiple land use changes using cellular automata (CA) is difficult because numerous spatial variables and parameters have to be utilized. Conventional CA models have problems in defining simulation parameter values, transition rules and model structures. In this paper, a three-layer neural network with multiple output neurons is designed to calculate conversion probabilities for competing multiple land uses. The model involves iterative looping of the neural network to simulate gradual land use conversion processes. Spatial variables are not deterministic because they are dynamically updated at the end of each loop. A GIS is used to obtain site attributes and training data, and to provide spatial functions for constructing the neural network. The parameter values for modelling are automatically generated by the training procedure of neural networks. The model has been successfully applied to the simulation of multiple land use changes in a fast growing area in southern China.