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Water 2017, 9(7), 524; https://doi.org/10.3390/w9070524

Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks

Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, China
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Received: 25 May 2017 / Revised: 12 July 2017 / Accepted: 13 July 2017 / Published: 14 July 2017
(This article belongs to the Special Issue Water Quality Monitoring and Modeling in Lakes)
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Abstract

One of the most important water quality problems affecting lakes and reservoirs is eutrophication, which is caused by multiple physical and chemical factors. As a representative index of eutrophication, the concentration of chlorophyll-a has always been a key indicator monitored by environmental managers. The most influential factors on chlorophyll-a may be dependent on the different water quality patterns in lakes. In this study, data collected from 27 lakes in different provinces of China during 2009–2011 were analyzed. The self-organizing map (SOM) was first applied on the datasets and the lakes were classified into four clusters according to 24 water quality parameters. Comparison amongst the clusters revealed that Cluster I was the least polluted and at the lowest trophic level, while Cluster IV was the most polluted and at the highest trophic level. The genetic algorithm optimized back-propagation neural network (GA-BPNN) was applied to each lake cluster to select the most influential input variables for chlorophyll-a. The results of the four clusters showed that the performance of GA-BPNN was satisfied with nearly half of the input variables selected from the predictor pool. The selected factors varied for the lakes in different clusters, which indicates that the control for eutrophication should be separate for lakes in different provinces of one country. View Full-Text
Keywords: self-organizing map; optimized back-propagation neural network; chlorophyll-a prediction; trophic levels of lakes self-organizing map; optimized back-propagation neural network; chlorophyll-a prediction; trophic levels of lakes
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Li, X.; Sha, J.; Wang, Z.-L. Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks. Water 2017, 9, 524.

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