改进CA-Markov模型的城市土地空间结构演变预测

Improved CA-Markov Model for Predicting Evolution of Urban Land Spatial Structure

城市土地空间结构演变预测需要大量的数据支持,现阶段预测手段存在预测精度不佳的问题,为此提出GIS技术下城市土地空间结构演变预测方法。通过GIS技术获取城市土地空间结构数据,并对其进行结构演变分析;通过人工神经网络模型、轮盘竞争机制和惯性系数对CA模型实行优化处理;将改进CA模型与Markov结构结合,建立改进CA-Markov模型,根据城市土地空间结构演变分析结果实现城市土地空间结构演变预测。实验结果表明,所提方法的城市土地空间结构演变预测精度更高,整体应用效果更好。

The prediction for urban land spatial structure evolution requires a large amount of data.Currently,some prediction methods only have low prediction accuracy.Therefore,a prediction method for urban land spatial structure evolution was proposed.Firstly,urban land spatial structure data was obtained by GIS technology,and then its structural evolution was analyzed.Moreover,the CA model was optimized through an artificial neural network model,roulette competition mechanism,and inertia coefficient.Meanwhile,the improved CA model was combined with Markov structure to construct an improved CA Markov model.Based on the analysis result,the evolution of urban land spatial structure was predicted.Experimental results show that the proposed method has higher accuracy in predicting the evolution of urban land spatial structure and better overall application effect.