Study on Tendency of Urban Coordinated Development Based on RBF Neural Network

Rapid urbanization has posed a considerable impact on the earth such as environment deterioration and resources degradation. Effectively predicting and evaluating the development trend of urban coordinated degree are key issues for urban sustainable development. In the paper, Important impact factors of coordinated development degree are obtained from grey relational analysis (GRA), and a Radial Basis Function Neural Network (RBFNN) model was built to simulate the trends of coordinated development of Wuhan city during 2006~2015. Results showed that three indexes, such as comprehensive utilization ratio of solid waste, unit GDP energy consumption and Gini coefficients, are the most important impact factors; Wuhan urban development would show a good coordination situation during studied period, but the coordinated development degree values predicted for 2010 (0.6824) and 2015 (0.7653) indicated that the city should be in the situation of primary coordination (2010) and intermediate coordination (2015). In the end, a series of countermeasures to improve the situation of development were further proposed.