Urban sprawl forecasting method based on cloud model cellular automata

Abstract

The invention discloses an urban sprawl forecasting method based on cloud model cellular automata. The method comprises the following steps: 1) rasterizing an urban land-use map, so that each grid is corresponding to a cellular; 2) carrying out 'near, mid and far' expressions on the variable of each cellular transition probability and the spatial distance between a non-city cellular and each space variable by using conceptual sets, and quantitatively expressing the conceptual sets by using cloud models; 3) building a rule generator; 4) obtaining cellular transition probabilities under the influence of space variables by the rule generator; and 5) making a behavior change rule of the non-city cellular, and according to the behavior change rule of the non-city cellular, calculating the final cellular transition probability of each cellular. In the method disclosed by the invention, a quantitative model between the uncertainty and the accuracy of analogue results is built, and the quantitative model is applied to the urban sprawl forecasting, thereby facilitating the more genuine and accurate simulation of urban sprawl.

Description

Technical field [0001] The invention relates to a city expansion prediction method, in particular to a city expansion prediction method based on a cloud model cellular automaton. Background technique [0002] Urban expansion is a hot area in the application of cellular automata (CA). Academician Tobler first introduced the cellular automata method to urban research in the 1970s, and used the cellular automata to simulate the rapid expansion of cities in the Detroit region of the Great Lakes in the United States. However, at this time, the application of cellular automata in urban expansion is still in the stage of general discussion and method "geographicalization". The performance and practicability of the standard cellular automata model in the field of urban expansion need to be further improved. Many extended cellular automata models have been proposed and developed more extensively. First, multi-criteria judgment [1] , Logistic regression [2] Mathematical statistics method...

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Problems solved by technology

[0003] Scholars represented by Li Xia, Ye Jiaan, and Zhou Chenghu have made many meaningful theoretical attempts on the cellular automata extension model, but these cellular automata extension models rely too much on efficient model algorithms and cannot fundamentally solve urban The reality problem of extended cellular automata, rarely considering the uncertain problem in urban expansion

The general cellular automata adopts the Monte Carlo method [9] , fuzzy logic [10-11] to reflect the uncertainty of urban expansion, but the above method only reflects the randomness or fuzziness of urban expansion uncertainty, and cannot fully express the uncertainty

Zhang et al. [12] The location and quantity Kappa coefficients are used to analyze the prediction uncertainty and simulation accuracy in the spatially explicit model. The essence of this research is to conduct sensitivity analysis on the simulation results, and the uncertainty quantitative propagation problem is not reflected in the model construction.

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