Method for dynamically simulating land future use scenario

Abstract

The present invention proposes and discloses a method for dynamically simulating a land future use scenario. The method integrates a capability that a cellular automaton (CA) simulates a complex systematic space-time evolution process and an advantage that a CLUE-S model simulates multiple types of land use type competitions; and the method overcomes certain inherent defects of a traditional CA or a pure CLUE-S model through advantage integration; in addition, intelligent computation of a distributional probability is realized by adopting an artificial neural network (ANN) algorithm; synchronous simulation of multiple land use changes is realized by introducing a competition mechanism of a roulette, so that the new method is more suitable for multiple types of land use data; and moreover, compared with a traditional model, the method has the practical advantages of higher precision, suitability for multiple scales, low data demand, few parameters, simple and convenient operation, high speed and the like. According to the method for dynamically simulating the land future use scenario, the intelligent algorithm (ANN) and the uncertainty model (the roulette) are effectively combined; and the method is applied to the prediction of the land future use scenario.

Description

technical field [0001] The invention belongs to the field of geographic information science and technology, and specifically relates to a method for dynamically simulating future land use scenarios by introducing a neural network model and a roulette competition model. The method is suitable for multi-scale, multi-region, multi-factor and multi-category land competition Geodynamic simulation. Background technique [0002] In the simulation of urban land use change, cellular automaton (CA) model and CLUE-S model are the mainstream models in the study of land use change simulation. Cellular automata is a space-time discrete dynamic model with strong space computing ability, which plays an important role in complexity science. Often used in self-organizing analysis processes. Cellular automata focuses on the microcosm, bottom-up, and fully embodies the idea that the individual and local behaviors of complex systems will produce a global and orderly pattern. Cellular automata...

AI Technical Summary

Problems solved by technology

The CLUE-S model has high accuracy for the simulation of regional land use change at the county scale, but when the scale is further reduced, the accuracy of the model will be significantly reduced

The CLUE-S model is subject to its artificially set parameters, and the uncertain parameters will lead to a certain degree of influence on the simulation results

In addition, the significance of the iteration factor of the CLUE-S model has no practical significance in the model

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