基于改进BP神经网络模型的云南文山州水资源脆弱性综合评价

利用层次分析法构建符合丰水地区水资源脆弱性评价的指标体系和等级标准，分别构建基于单、双隐层BP神经网络技术的区域水资源脆弱性综合评价模型，并采用内插法构造网络训练样本，将水资源脆弱性分级评价标准值作为“评价”样本，对云南文山州区域水资源脆弱性进行评价分析。结果表明：①单、双隐层BP神经网络模型对区域水资源脆弱性综合评价结果基本相同，说明研究建立的区域水资源脆弱性评价模型和评价方法均是合理可行的，与单隐层网络相比，双隐层网络泛化能力强，预测精度高，但训练时间较长；②文山州各评价区域不同规划水平年水资源脆弱性评价等级为Ⅲ-Ⅴ级，即处于中度脆弱与不脆弱之间，客观反映了该州水资源脆弱性状况，符合区域实际情况。评价结果可以作为研究和评价区域水资源脆弱性的参考依据。

Comprehensive Assessment of the Vulnerability of Water Resources by Improved BP Neural Network Model

The vulnerability of water resources in Wenshan autonomous prefecture was evaluated. Analytic hierarchy process was firstly employed to determine the assessment indexes and grades suitable for water-abundant areas. In subsequence, comprehensive evaluation models for regional water vulnerability were established based on single and double hidden layer BP neural network respectively. Interpolation was used to construct network training samples, and the grading standard value was taken as the evaluation sample. Results showed that: 1) the assessment results of both models were generally the same, indicating that the models and evaluation methods were reasonable and feasible. Compared with single hidden layer network, double hidden layer network has better generalization ability and higher prediction accuracy, but longer training time; 2) The evaluated vulnerability of water resource in Wenshan in different target years was between grade Ⅲ and grade Ⅴ, which meant invulnerable to moderately vulnerable. The results objectively reflect the vulnerability of water resources  in the region and can serve as a reference for research and evaluation.