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Modeling chlorophyll-a concentrations using an artificial neural network for precisely eco-restoring lake basin

文献类型： 外文期刊

作者： Lu, Fang 1 ; Chen, Zhi 1 ; Liu, Wenquan 2 ; Shao, Hongbo 3 ;

作者机构： 1.Concordia Univ, Dept Bldg Civil & Environm Engn, Montreal, PQ H3G 1M8, Canada

2.SOA, Inst Oceanog 1, Key Lab Marine Sedimentol & Environm Geol, Qingdao 266061, Peoples R China

3.Jiangsu Acad Agr Sci, Inst Agrobiotechnol, Nanjing 210014, Jiangsu, Peoples R China

关键词： Back-propagation artificial neural network;Chlorophyll-a;Prediction;Lake water quality;Eco-restoring

期刊名称：ECOLOGICAL ENGINEERING （ 影响因子：4.035； 五年影响因子：4.611 ）

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收录情况： SCI

摘要： A back-propagation artificial neural network (BPANN) model was developed in this study for the prediction of chlorophyll-a concentration in Lake Champlain. 21 years of monitoring data (1992-2012) of water quality parameters was used to train, validate and test the BPANN models. The optimal input parameters of the model were selected on the basis of the performance of models built with different combinations of input variables. To verify the model performances, the trained models were applied to field monitoring data from Lake Champlain. Prediction accuracy was measured by using the coefficient of determination (R-2) and RMSE-observations standard deviation ratio (RSR). The R-2 values of the best-performed model in the training set, validation set, testing set, and all-year data were 0.82, 0.93, 0.81, and 0.87, respectively. The corresponding RSR values of the three data sets and all-year were 0.62, 0.38, 0.53, and 0.48, respectively. Results indicated that the developed BPANN model can predict chlorophyll-a concentrations in Lake Champlain with high accuracy and provide a quick assessment of chlorophyll-a variation for lake management and eco-restoration. (C) 2016 Elsevier B.V. All rights reserved.

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