基于PCR和RBF神经网络的城市智能增长水平评价模型

A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

城市智能增长已经成为城市规划者和决策者广泛采用的一种建设城市的环保方式,这对衡量城市智能增长水平具有实际意义。在本文中,定义理性度(RD)来描述城市智能增长的水平,通过主成分回归(PCR)和径向基函数(RBF)神经网络建立RD评价模型。以玉门和Otago为例进行研究,二者的RD值分别为0.04482和0.04591,这表明这2个城市智慧增长计划都取得了一定的成功,Otago的城市发展水平优于玉门,同时研究发现玉门应优先考虑城市绿化与环境保护,而Otago则要优先考虑经济发展。本文模型为追求科学智能增长的城市提供了有力的参考。

Urban smart growth has become an environmental protection method widely used by urban planners and decision makers to build cities,which has practical significance to measure the level of urban smart growth.In this paper,rational degree(RD)is defined to describe the level of urban smart growth,then RD model is established through principal component regression(PCR)and radial basis function(RBF)neural network.Taking Yumen and Otago as examples,their RD values are 0.04482 and 0.04591 respectively,which indicates that both cities’smart growth development mode has achieved certain success.The urban development level of Otago is better than that of Yumen.At the same time,this study finds that Yumen should give priority to urban greening and environmental protection,while Otago should give priority to economic development.The proposed model provides a powerful reference for the cities to pursue scientific and smart growth.