Elman与GRNN神经网络模型在水环境承载力评价中的应用——以文山州区域水环境承载力评价为例

利用层次分析法构建符合区域水环境承载力的评价指标体系和分级标准,基于Elman神经网络与广义回归神经网络(GRNN)算法原理,提出Elman与GRNN神经网络水环境承载力评价模型,采用内插法构造网络训练样本,将水环境承载力分级评价标准阈值样本进行评价,将结果作为区域水环境承载力等级评价的划分依据,对文山州不同规划水平年水环境承载力进行评价。结果表明:文山州不同规划水平年水环境承载力处于绝对可承载与基本可承载之间,客观反映了区域水环境现状及规划期望效果,可为区域水环境承载力评价和研究提供参考。Elman与GRNN神经网络模型评价结果基本相同,表明研究建立的区域水环境承载力评价模型和评价方法均是合理可行的,二者均可作为区域水环境承载力评价的选用模型。

Application of neural network model of Elman and GRNN in evaluation of water environment carrying capacity

The evaluation index system and grading standards of regional water environment carrying capacity were built by using analytic hierarchy process. Based on the algorithm principle of Elman neural network and generalized regression neural network ( GRNN ), the paper proposed the evaluation model of water environment carrying capacity of Elman and GRNN neural network, used the interpolation method to construct network training samples, reviewed the threshold sample of water environment carrying capacity evaluation standard, took the results as the division basis to evaluate the carrying capacity class of a regional water environment, and evaluated the water environment carrying capacity of Wenshan in different level years. The results show that the water environment carrying capacity of Wenshan state in different level years is between absolute bearing and basic bearing, witch objectively reflects the present situation and plan desired effect of regional water environment, and can provide reference for the evaluation and research of regional water environment carrying capacity. The evaluation results of Elman and GRNN neural network models are similar witch shows that the model and method of evaluation of regional water environment carrying capacity established by study are reasonable and feasible, the two can be used as the evaluation model of regional water environment carrying capacity.