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.
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.
Xu
Min
M
College of Environmental Science and Engineering, Hunan University, Changsha 410082, China. ykxumin@21cn.com
Zeng
Guang-ming
GM
Xu
Xin-yi
XY
Huang
Guo-he
GH
Sun
Wei
W
Jiang
Xiao-yun
XY
D001499:Bayes Theorem; D002681:China; D002734:Chlorophyll / Q000378:metabolism*; D000077194:Chlorophyll A; D004463:Ecology / Q000379:methods*; D004784:Environmental Monitoring / Q000379:methods*; D005068:Eutrophication / Q000502:physiology*; D005618:Fresh Water / Q000382:microbiology*; D016014:Linear Models; D008954:Models, Biological; D016571:Neural Networks, Computer
D002734:Chlorophyll; D000077194:Chlorophyll A