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针对水资源评价的复杂性不确定性等问题,提出了一种粗集与BP神经网络相结合的城市水资源可持续利用评价模型。利用粗糙集属性约简算法对城市水资源评价指标约简,找出主要评价指标,以简化BP网络的输入层;利用BP神经网络的非线性适应性信息处理能力对评价数据进行量化训练。基于湖北省2004~2010年城市水资源的相关数据,利用Matlab仿真平台进行评价,实证结果验证了该改进模型的科学性和有效性,为城市水资源可持续利用评价提供了有效的方法。
In view of the complexity of water resources evaluation uncertainty and other issues, a rough set and BP neural network for sustainable use of water resources evaluation model is proposed. Rough set attribute reduction algorithm is used to reduce the evaluation index of urban water resources and find out the main evaluation indexes so as to simplify the input layer of BP network. The evaluation data is quantified using the nonlinear adaptive information processing ability of BP neural network. Based on the data of urban water resources in Hubei province from 2004 to 2010, the paper uses Matlab simulation platform to evaluate. The empirical results verify the scientific and effective of the improved model and provide an effective method for the sustainable use of urban water resources.