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| Water quality forecasting based on multilayer fully connected neural network for Baiyangdian Lake |
| Received:March 16, 2020 |
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| KeyWord:fully connected neural network;deep learning;Baiyangdian Lake;water quality forecasting |
| Author Name | Affiliation | E-mail | | LIU Shi-cun | State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China | | | YANG Wei | State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China | | | TIAN Kai | State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China | | | WANG Huan-huan | State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China | | | ZHAO Yan-wei | State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China | awei1974@bnu.edu.cn | | ZHU Xiao-lei | China Xiong'an Group Ecological Construction Investment Co. Ltd, Baoding 071700, China | |
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| Abstract: |
| Baiyangdian Lake, the largest shallow lake wetland in the North China Plain, is the most important ecological support of Xiong'an New Area. Forecasting water quality can provide important information for the environmental restoration and management of Baiyangdian Lake. This study used water quality data, including BOD, COD, TN, and TP values, obtained from the sub-lakes of Nanliuzhuang, Quantou, and Shaochedian between 1996 and 2015, to develop a fully connected neural network model for water quality predication. After model calibration and validation, we used the model to predict the changes in water quality over the next three years. The results show that all the water quality indices are improving in the sub-lakes of Nanliuzhuang, Quantou, and Shaochedian, but TN and TP still exceed their standards at some monitoring sites. In the future, we should strengthen pollution control in both the flowing rivers and Baiyangdian Lake, and increase environmental flow releases, as well as the hydrological connection between the rivers and lake. |
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