[目的]利用BP人工神经网络模型预测太湖水污染指标,为探讨湖泊水污染物变化规律提供参考.[方法]利用2004～2010年浙江嘉兴王江泾断面自动监测站4项水质指标,建立了太湖水污染BP人工神经网络模型,并对太湖2012年前5周的水质情况进行预测.[结果]建立了浙江嘉兴王江泾断面的4项水质指标浓度的三层BP神经网络预测模型,其预测精度较高,对湖泊水环境污染物预测的适应性较好；对太湖2012年前5周的水质情况进行预测,结果表明,2012年前5周水质污染情况加重,基本为V类水质,符合太湖水质污染情况发展态势.[结论]BP人工神经网络具有很强的非线性映射能力和柔性的网络结构,与传统的统计建模方法相比,其预测精度较高,能较好地反映水质指标的内在变化规律,为控制水环境污染提供了科学预测方法.%[Objective]The present study was conducted to predict water pollutant concentrations in Taihu Lake using BP neural network model in order to find out the mechanism of changes in water pollutant concentrations in lakes. [Method] The BP neural network forecast method for predicting water pollutant concentration in Taihu Lake was established on the basis of 4 water quality indices, viz., pH value, dissolved oxygen, CODMn and NH3-N from 2004-2010. The data were obtained from section automatic monitoring station of Wang River (Jiaxing, Zhejiang). The water quality of Wang River during first 5 weeks of 2012 was predicted by using the established model. [Result Jin order to simplify the structure of BP neural network model and improve the prediction speed, the study established a three-layer BP neural network model. The predicted results were found accurate and the model was found to efficiently predict the changes in water pollutant concentration in lakes. The predicted results of water quality of Wang River during first 5 weeks of 2012 showed that the water quality of Wang River belonged to V grade and became worse. The prediction results were in accordance with the development trend of water pollutants in Taihu Lake. [ Conclusion ]BP neural network model had good nonlinear mapping capability and flexible network structure. It could better reflect the changes patterns of water quality index with high prediction precision and provide a scientific prediction mechanism for controlling water pollution.
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