遗传-BP神经网络法预测叶绿素a浓度变化

基于2009年—2010年对临江河回水区水质指标的监测数据,采用遗传算法结合BP神经网络的方法对回水区的叶绿素a(Chl-a)浓度变化进行动态模拟预测。通过灰色关联法确定了对Chl-a浓度有显著影响的指标与网络输入变量,即水温、DO、流速、透明度(SD)、TP、CODMn及Chl-a。模拟结果表明,遗传-BP神经网络的预测值和实测值吻合较好,其相对误差约为9.8%,模型可良好地用于次级河流回水区叶绿素a浓度的短期预测。预测结果表明,在春末夏初季节,当水库蓄水位为150～160 m时,临江河回水区富营养化潜势较高,尤其应注重临江河该时段富营养化的防控工作。

Prediction of Chlorophyll-a by Genetic BP Neural Network

Based on the monitoring data of water quality in the backwater area of Linjiang River from 2009 to 2010,the dynamic variation of chlorophyll-a concentration in the backwater area was simulated and predicted by genetic algorithm with BP neural network.The input variables of the network which have significant effects on chlorophyll-a concentrations were determined by grey relational analysis,and they were water temperature,dissolved oxygen(DO),flow rate,transparency(SD),total phosphorus(TP),CODMn and chlorophyll-a.The simulation results indicate that the predictive values of genetic BP neural network and measured values fit well with the relative error of 9.8%.The prediction model can be well used for short-term prediction of chlorophyll-a concentration in backwater area of tributary.The prediction results show that the backwater area of Linjiang River has high eutrophication potential when the reservoir water level is in the range of 150 to 160 m during the late spring and early summer.Particular emphasis on prevention and control of eutrophication of Linjiang River should be given during this period.