Water 2018, 10(1), 83; https://doi.org/10.3390/w10010083
Using a Backpropagation Artificial Neural Network to Predict Nutrient Removal in Tidal Flow Constructed Wetlands
Wei Li 1,2,3
, Lijuan Cui 1,2,3,*
, Yaqiong Zhang 1,2,3, Zhangjie Cai 1,2,3, Manyin Zhang 1,2,3, Weigang Xu 1,2,3, Xinsheng Zhao 1,2,3, Yinru Lei 1,2,3, Xu Pan 1,2,3, Jing Li 1,2,3 and Zhiguo Dou 1,2,3
, Lijuan Cui 1,2,3,*
, Yaqiong Zhang 1,2,3, Zhangjie Cai 1,2,3, Manyin Zhang 1,2,3, Weigang Xu 1,2,3, Xinsheng Zhao 1,2,3, Yinru Lei 1,2,3, Xu Pan 1,2,3, Jing Li 1,2,3 and Zhiguo Dou 1,2,31
Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China
2
The Beijing Key Laboratory of Wetland Ecological Function and Restoration, Beijing 100091, China
3
Beijing Hanshiqiao National Wetland Ecosystem Research Station, Beijing 101399, China
*
Author to whom correspondence should be addressed.
Received: 3 November 2017 / Revised: 2 January 2018 / Accepted: 14 January 2018 / Published: 18 January 2018
(This article belongs to the Special Issue Integrated Constructed Wetland Systems: Design, Operation, and Performance)
Abstract
Nutrient removal in tidal flow constructed wetlands (TF-CW) is a complex series of nonlinear multi-parameter interactions. We simulated three tidal flow systems and a continuous vertical flow system filled with synthetic wastewater and compared the influent and effluent concentrations to examine (1) nutrient removal in artificial TF-CWs, and (2) the ability of a backpropagation (BP) artificial neural network to predict nutrient removal. The nutrient removal rates were higher under tidal flow when the idle/reaction time was two, and reached 90 ± 3%, 99 ± 1%, and 58 ± 13% for total nitrogen (TN), ammonium nitrogen (NH4+-N), and total phosphorus (TP), respectively. The main influences on nutrient removal for each scenario were identified by redundancy analysis and were input into the model to train and verify the pollutant effluent concentrations. Comparison of the actual and model-predicted effluent concentrations showed that the model predictions were good. The predicted and actual values were correlated and the margin of error was small. The BP neural network fitted best to TP, with an R2 of 0.90. The R2 values of TN, NH4+-N, and nitrate nitrogen (NO3−-N) were 0.67, 0.73, and 0.69, respectively. View Full-TextKeywords:
tidal flow; constructed wetlands; nutrients removal; BP neural network
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Li, W.; Cui, L.; Zhang, Y.; Cai, Z.; Zhang, M.; Xu, W.; Zhao, X.; Lei, Y.; Pan, X.; Li, J.; Dou, Z. Using a Backpropagation Artificial Neural Network to Predict Nutrient Removal in Tidal Flow Constructed Wetlands. Water 2018, 10, 83.
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