基于概率神经网络的文山州水资源配置合理性评价分析

通过分析概率神经网络(以下称PNN)的基本结构及其训练算法，依据水资源丰沛区水资源合理配置评价指标标准，建立PNN水资源合理配置评价模型，对文山州不同规划水平年水资源配置的合理性进行综合评价。结果表明：①不同规划水平年各评价区域水资源配置评价为3~7级，即处于基本合理与合理之间，基本反映了文山州现状及中、长期水资源配置状况，符合区域发展现状，说明研究建立的PNN评价模型和评价方法是合理可行的。②概率神经网络模型在分类精度上优于误差反向传播神经网络模型，且方法简单可行，运算时间短，不存在局部最优值，能够有效实现对水资源配置合理性的综合评价，是一种可以运用的区域水资源配置合理性评价方法。

Evaluation of Rational Water Allocation Based on Probabilistic Neural Network:Case Study of Wenshan Prefecture

To evaluate the bearing capacity of water resources objectively,a PNN(probabilistic neural network)-based model was established in line with the evaluation criteria for rational water allocation in water-abundant areas.The model was applied to evaluate the rationality of water allocation in different target years in Wenshan prefecture.Results showed that the water allocation in different target years was between level 3-level 7,which suggests a basically-rational to rational water allocation.It also indicated that the fairness of water allocation,the efficiency of water consumption and the coordination between utilization and supply needs to be improved.The results largely reflect the status quo and medium-to-long-term condition of water allocation in the prefecture.Moreover,the PNN-based model is superior to the error backpropagation model in terms of classification precision.The model is simple and feasible,with short operation time and no local optimum value,thereby could effectively evaluate the rationality of regional water allocation.