GEO-ENVIRONMENTAL SUITABILITY EVALUATION OF LAND FOR URBAN CONSTRUCTION BASED ON A BACK-PROPAGATION NEURAL NETWORK AND GIS: A CASE STUDY OF HANGZHOU

Evaluating the geo-environmental suitability of land for urban construction is an important step in the analysis of urban land use potential. Using geo-environmental factors and the land use status of Hangzhou, China, a back-propagation (BP) neural network model for the evaluation of the geo-environmental suitability of land for urban construction was established with a geographic information system (GIS) and techniques of grid, geospatial, and BP neural network analysis. Four factor groups, comprising nine separate subfactors of geo-environmental features, were selected for the model: geomorphic type, slope, site soil type, stratum steadiness, Holocene saturated soft soil depth, groundwater abundance, groundwater salinization, geologic hazard type, and geologic hazard degree. With the support of the model, the geo-environmental suitability of Hangzhou land for urban construction was divided into four suitability zones: zone I, suitable for super high-rise and high-rise buildings; zone II, suitable for multi-story buildings; zone III, suitable for low-rise buildings; and zone IV, not suitable for buildings. The results showed that a BP neural network can capture the complex non-linear relationships between the evaluation factors and the suitability level, and these results will support scientific decision-making for urban-construction land planning, management, and rational land use in Hangzhou.