Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks
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 man...
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doaj-722bfe6d03ba45f9869b68f6b534d7db2020-11-24T22:16:36ZengMDPI AGWater2073-44412017-07-019752410.3390/w9070524w9070524Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural NetworksXue Li0Jian Sha1Zhong-Liang Wang2Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, ChinaTianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, ChinaTianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, ChinaOne 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.https://www.mdpi.com/2073-4441/9/7/524self-organizing mapoptimized back-propagation neural networkchlorophyll-a predictiontrophic levels of lakes |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xue Li Jian Sha Zhong-Liang Wang |
spellingShingle |
Xue Li Jian Sha Zhong-Liang Wang Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks Water self-organizing map optimized back-propagation neural network chlorophyll-a prediction trophic levels of lakes |
author_facet |
Xue Li Jian Sha Zhong-Liang Wang |
author_sort |
Xue Li |
title |
Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks |
title_short |
Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks |
title_full |
Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks |
title_fullStr |
Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks |
title_full_unstemmed |
Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks |
title_sort |
chlorophyll-a prediction of lakes with different water quality patterns in china based on hybrid neural networks |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2017-07-01 |
description |
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. |
topic |
self-organizing map optimized back-propagation neural network chlorophyll-a prediction trophic levels of lakes |
url |
https://www.mdpi.com/2073-4441/9/7/524 |
work_keys_str_mv |
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1725788873463693312 |