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首页> 外文期刊>Journal of Environmental Sciences >Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-A case study of chlorophyll- a prediction in Nanzui water area of Dongting Lake
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Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-A case study of chlorophyll- a prediction in Nanzui water area of Dongting Lake

机译：贝叶斯正则化BP神经网络模型在水生生态数据分析中的应用-以叶绿素为例-洞庭湖南嘴水域预测

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摘要

Bayesian regularized BP neural network(BRBPNN) technique was applied in the chlorophyll-a prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS 11.0 software, the BRBPNN model was established between chlorophyll-a and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.00078426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll-a declined in the order of alga amount > secchi disc depth(SD) > electrical conductivity (EC). Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-a concentration, total phosphorus(TP) and total nitrogen(TN) were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data( chlorophyll- a prediction) and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.
机译：贝叶斯正则BP神经网络（BRBPNN）技术应用于洞庭湖南嘴水域叶绿素的预测。通过BP网络插值法，获得了网络的输入和输出样本。在SPSS 11.0软件中使用逐步/多元线性回归方法选择输入变量后，在叶绿素a与环境参数，生物学参数之间建立了BRBPNN模型。获得的最佳网络结构为3-11-1，训练集和测试集的相关系数和均方误差分别为0.999和0.00078426、0.981和0.0216。每个输入神经元与不同结构的最佳BRBPNN模型的隐藏层之间的平方权重之和表明，单个输入参数对叶绿素-a的影响按藻类量> secchi盘深度（SD）>电导率的顺序降低（ EC）。另外，这也表明这三个因素对叶绿素-a浓度变化的贡献最大，总磷（TP）和总氮（TN）最小。所有结果表明，BRBPNN模型能够自动进行正则化参数选择，因此可以确保出色的生成能力和鲁棒性。因此，本研究为BRBPNN模型在水生生态数据分析（叶绿素a的预测）中的应用以及对洞庭湖南嘴水域有效富营养化处理措施的解释奠定了基础。

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