Urban land use suitability evaluation is the basic work of urban land use planning and management. The evaluation method is a core in urban land use suitability evaluation. Traditional urban land use suitability evaluation methods are GIS-based methods which often can not get satisfactory results for the complex nonlinear urban land use system. Artificial neural network is a frontier theory of complex non-linearity scientific and artificial intelligence science. It is a new method to evaluate urban land use suitability. This paper took the land use suitability evaluation of Hefei city as an example, building a back propagation neural network with 8 neurous of input layer, 5 neurons of hide layer and 3 neurons of output layer. The analysis shows: the high suitability area is 682.27 km2in Hefei city, being about 8.73% of the total study area; the middle suitability area is 5965.76 km2, or about 76.33% of the total area and the low suitability area is 1167.35 km2, or about 14.94% of the total area. The results reflect the actual situation in Hefei city. The study shows that the back propagation neural network model can overcome the shortcomings of traditional evaluation methods. It means that artificial neural network is suitable for urban land use suitability evaluation. This reflects that artificial neural network has great academic value and application prospect in urban land use suitability evaluation. It also reflects that this study can provide a new idea and method for urban land use suitability evaluation.
Degree of Harmony of Urban Land Use and Economic Development: Case Study of Wuhan Metropolitan Area