A Land Use Change Simulation Model: Coupling of Evolutionary Algorithm and FLUS Model

It is of great significance to study how to set parameters of land use change simulation models more scientifically and objectively, in order to avoid the problem of poor simulation caused by improper parameters setting in a complex model. In this paper, the EA- FLUS model with parameter optimization function was constructed by coupling Evolutionary Algorithm (EA) and FLUS model. This model first optimized the parameters of the artificial neural network model in the FLUS model through evolutionary strategy to improve the prediction accuracy of the probability distribution of each land use type. On this basis, combined with geospatial partition, the parameters of the cellular automaton model in the FLUS model were adjusted by using the combination of elitist genetic algorithm and evolutionary strategy to improve the simulation accuracy. In the empirical study phase, taking Guilin as the study area, this paper analyzed the improvement of EA-FLUS model by partition simulation of land use change. In addition, the natural development scenario, cultivated land protection scenario, and ecological priority scenario were set up to simulate the land use change in Guilin from 2020 to 2030. The results show that: (1) Compared with the parameters setting based on experience and historical characteristics of land use change, the parameters optimization result using evolutionary algorithms was closer to the policy orientation in the study area, and better reflected the diversified development trends of various land use types in different geospatial partition; (2) Compared with the FLUS model, the EA- FLUS model had more advantages in land use change simulation with geospatial partition. The overall accuracy, Kappa coefficient, and FoM coefficient of the simulation result were increased by 0.56%, 0.011, and 0.009, respectively; (3) The construction land and cultivated land in Guilin showed a strong expansion trend, but the forested land showed a shrinking trend. Further strengthening the protection of ecological space would help to slow down the expansion of construction land and cultivated land. The research results not only enrich the existing land use change simulation techniques and methods, but also provide a certain theoretical basis and scientific basis for urban planning and sustainability research. © 2023 Journal of Geo-Information Science. All rights reserved.