Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in Beijing
Nowadays, freshwater resources are facing numerous crises and pressures, resulting from both artificial and natural process, so it is crucial to predict the water quality for the department of water environment protection. This paper proposes a hybrid optimized algorithm involving a particle swarm o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/9/1863 |
| _version_ | 1819057154619867136 |
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| author | Jianzhuo Yan Zongbao Xu Yongchuan Yu Hongxia Xu Kaili Gao |
| author_facet | Jianzhuo Yan Zongbao Xu Yongchuan Yu Hongxia Xu Kaili Gao |
| author_sort | Jianzhuo Yan |
| collection | DOAJ |
| description | Nowadays, freshwater resources are facing numerous crises and pressures, resulting from both artificial and natural process, so it is crucial to predict the water quality for the department of water environment protection. This paper proposes a hybrid optimized algorithm involving a particle swarm optimization (PSO) and genetic algorithm (GA) combined BP neural network that can predict the water quality in time series and has good performance in Beihai Lake in Beijing. The data sets consist of six water quality parameters which include Hydrogen Ion Concentration (pH), Chlorophyll-a (CHLA), Hydrogenated Amine (NH4H), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and electrical conductivity (EC). The performance of the model was assessed through the absolute percentage error (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>A</mi> <mi>P</mi> <msub> <mi>E</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (<inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula>). Study results show that the model based on PSO and GA to optimize the BP neural network is able to predict the water quality parameters with reasonable accuracy, suggesting that the model is a valuable tool for lake water quality estimation. The results show that the hybrid optimized BP model has a higher prediction capacity and better robustness of water quality parameters compared with the traditional BP neural network, the PSO-optimized BP neural network, and the GA-optimized BP neural network. |
| first_indexed | 2024-12-21T13:34:48Z |
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| id | doaj.art-f81f3d7956f54166a9fa9bd4dd2e644a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-21T13:34:48Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f81f3d7956f54166a9fa9bd4dd2e644a2022-12-21T19:02:12ZengMDPI AGApplied Sciences2076-34172019-05-0199186310.3390/app9091863app9091863Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in BeijingJianzhuo Yan0Zongbao Xu1Yongchuan Yu2Hongxia Xu3Kaili Gao4Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaNowadays, freshwater resources are facing numerous crises and pressures, resulting from both artificial and natural process, so it is crucial to predict the water quality for the department of water environment protection. This paper proposes a hybrid optimized algorithm involving a particle swarm optimization (PSO) and genetic algorithm (GA) combined BP neural network that can predict the water quality in time series and has good performance in Beihai Lake in Beijing. The data sets consist of six water quality parameters which include Hydrogen Ion Concentration (pH), Chlorophyll-a (CHLA), Hydrogenated Amine (NH4H), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and electrical conductivity (EC). The performance of the model was assessed through the absolute percentage error (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>A</mi> <mi>P</mi> <msub> <mi>E</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (<inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula>). Study results show that the model based on PSO and GA to optimize the BP neural network is able to predict the water quality parameters with reasonable accuracy, suggesting that the model is a valuable tool for lake water quality estimation. The results show that the hybrid optimized BP model has a higher prediction capacity and better robustness of water quality parameters compared with the traditional BP neural network, the PSO-optimized BP neural network, and the GA-optimized BP neural network.https://www.mdpi.com/2076-3417/9/9/1863water quality predictionparticle swarm optimizationgenetic algorithmBP neural network |
| spellingShingle | Jianzhuo Yan Zongbao Xu Yongchuan Yu Hongxia Xu Kaili Gao Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in Beijing Applied Sciences water quality prediction particle swarm optimization genetic algorithm BP neural network |
| title | Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in Beijing |
| title_full | Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in Beijing |
| title_fullStr | Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in Beijing |
| title_full_unstemmed | Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in Beijing |
| title_short | Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in Beijing |
| title_sort | application of a hybrid optimized bp network model to estimate water quality parameters of beihai lake in beijing |
| topic | water quality prediction particle swarm optimization genetic algorithm BP neural network |
| url | https://www.mdpi.com/2076-3417/9/9/1863 |
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