未确知测度BP神经网络模型在黑河流域水质预测中的应用

水质预测是水环境规划、评价和管理的重要依据,对促进水资源可持续利用及生态发展具有重要意义。针对水质预测中各项因子的不确定性,基于未确知测度理论(unascertained measure,UM),采用改变网络初值的方法,对BP神经网络加以改进,并利用黑河流域莺落峡水文站1998~2011年的水质监测资料进行分析和预测。以挥发酚为参考序列,用灰色关联方法分析参考序列与其他因子的关联度,并最终确定BP网络的输入节点为CODmn、DO、SO42-、Cr6+以及挥发酚,输出节点为挥发酚,从而建立UMBP模型。分析结果表明,UM-BP预测模型比标准的BP神经网络模型具有更高的预测精度。因此,该模型应用于黑河流域水质预测是可行的。

Unascertained Measure BP Neural Network Model for Water Quality Prediction in Heihe River Basin

Water quality prediction is an important basis of water environment planning, evaluation and management, and has important significance to promote the sustainable utilization of water resources and ecological development. According to the uncertainty of various factors in water quality prediction, based on unascertained measure (UM) theory, the method of changing the initial value of the nework to improve BP neural network. And analysis and forecasting were made with the quality data from the Yingluoxia Station in the Heihe River Basin during 1998-2011. To analyze the correlation degree of reference sequence and other factors with gray correlation method (volatile phenol as the reference sequence), and determine the input nodes of BP network which includes CODmn, DO, SO4 2-, Cr6+ and volatile phenol, the output nodes is volatile phenol, thereby establish UM-BP model. The analysis results show that UM -BP prediction model has a higher prediction accuracy than standard BP neural network model. Therefore, the model used in water quality prediction in the Heihe River Basin is feasible.