City expansion multi-scenario simulation cellular automaton method based on cross entropy optimizer

Abstract

The invention relates to a city expansion multi-scenario simulation cellular automaton method based on a cross entropy optimizer, which comprises the following steps: 1) supervising and classifying satellite remote sensing images to obtain a land utilization classification map, and establishing spatial variable factor data; 2) acquiring effective sample points in the research area based on the spatial variable factor data; 3) establishing a CA city expansion simulation prototype model, and acquiring CA parameters based on the effective sample point data; 4) establishing a related objective function for optimizing CA parameters, and optimizing the CA parameters by using a cross entropy optimizer; 5) establishing a CA conversion rule, and obtaining a conversion probability graph; 6) establishing an urban expansion simulation CACEO model, and simulating and predicting urban expansion dynamics and future possible scenes; and 7) performing precision evaluation on the CACEO model and the simulation prediction result thereof, and outputting and storing the simulation result. Compared with the prior art, the method provided by the invention effectively optimizes the CA model and realizes multi-target city expansion scene prediction through objective weight determination.

Description

technical field [0001] The invention relates to a multi-scenario simulation method for urban expansion, in particular to a multi-scenario simulation cellular automata method for urban expansion based on a cross-entropy optimizer. Background technique [0002] Urban development is the result of changes in land use patterns, which in turn are strongly influenced by human activities and lead to a series of social and environmental problems. Currently, the rapid growth of the global urban population is creating a growing demand for urban land use that is expected to continue for decades. By building a model to improve the accuracy of land use simulation, it can serve as an important reference value for future regional land use planning and urban development decisions. CA is a space-time dynamic model with obvious space-time coupling characteristics, which is especially suitable for dynamic simulation of complex land use change research. CA models can be implemented using itera...

AI Technical Summary

Problems solved by technology

Most of these conversion rules are represented by mathematical formulas, and the determination of the parameters in the formulas is very difficult

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