Real-time prediction of river chloride concentration using ensemble learning. Zhang, Q., Li, Z., Zhu, L., Zhang, F., Sekerinski, E., Han, J., & Zhou, Y. Environmental Pollution, 291:118116, December, 2021. Publisher: Elsevierdoi abstract bibtex 1 download Real-time river chloride prediction has received a lot of attention for its importance in chloride control and management. In this study, an artificial neural network model (i.e., multi-layer perceptron, MLP) and a statistical inference model (i.e., stepwise-cluster analysis, SCA) are developed for predicting chloride concentration in stream water. Then, an ensemble learning model based on MLP and SCA is proposed to further improve the modeling accuracy. A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R2 with values of 11.58 mg/L, 27.55%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.
@article{ZhangEtAl21EnsembleLearning,
title = {Real-time prediction of river chloride concentration using ensemble learning},
volume = {291},
doi = {10.1016/j.envpol.2021.118116},
abstract = {Real-time river chloride prediction has received a lot of attention for its importance in chloride control and management. In this study, an artificial neural network model (i.e., multi-layer perceptron, MLP) and a statistical inference model (i.e., stepwise-cluster analysis, SCA) are developed for predicting chloride concentration in stream water. Then, an ensemble learning model based on MLP and SCA is proposed to further improve the modeling accuracy. A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R2 with values of 11.58 mg/L, 27.55\%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40\%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.},
journal = {Environmental Pollution},
author = {Zhang, Qianqian and Li, Zhong and Zhu, Lu and Zhang, Fei and Sekerinski, Emil and Han, Jing-Cheng and Zhou, Yang},
month = dec,
year = {2021},
note = {Publisher: Elsevier},
pages = {118116},
}
Downloads: 1
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A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R2 with values of 11.58 mg/L, 27.55%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.","journal":"Environmental Pollution","author":[{"propositions":[],"lastnames":["Zhang"],"firstnames":["Qianqian"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Zhong"],"suffixes":[]},{"propositions":[],"lastnames":["Zhu"],"firstnames":["Lu"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Fei"],"suffixes":[]},{"propositions":[],"lastnames":["Sekerinski"],"firstnames":["Emil"],"suffixes":[]},{"propositions":[],"lastnames":["Han"],"firstnames":["Jing-Cheng"],"suffixes":[]},{"propositions":[],"lastnames":["Zhou"],"firstnames":["Yang"],"suffixes":[]}],"month":"December","year":"2021","note":"Publisher: Elsevier","pages":"118116","bibtex":"@article{ZhangEtAl21EnsembleLearning,\n\ttitle = {Real-time prediction of river chloride concentration using ensemble learning},\n\tvolume = {291},\n\tdoi = {10.1016/j.envpol.2021.118116},\n\tabstract = {Real-time river chloride prediction has received a lot of attention for its importance in chloride control and management. In this study, an artificial neural network model (i.e., multi-layer perceptron, MLP) and a statistical inference model (i.e., stepwise-cluster analysis, SCA) are developed for predicting chloride concentration in stream water. Then, an ensemble learning model based on MLP and SCA is proposed to further improve the modeling accuracy. A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R2 with values of 11.58 mg/L, 27.55\\%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40\\%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.},\n\tjournal = {Environmental Pollution},\n\tauthor = {Zhang, Qianqian and Li, Zhong and Zhu, Lu and Zhang, Fei and Sekerinski, Emil and Han, Jing-Cheng and Zhou, Yang},\n\tmonth = dec,\n\tyear = {2021},\n\tnote = {Publisher: Elsevier},\n\tpages = {118116},\n}\n\n","author_short":["Zhang, Q.","Li, Z.","Zhu, L.","Zhang, F.","Sekerinski, E.","Han, J.","Zhou, Y."],"key":"ZhangEtAl21EnsembleLearning","id":"ZhangEtAl21EnsembleLearning","bibbaseid":"zhang-li-zhu-zhang-sekerinski-han-zhou-realtimepredictionofriverchlorideconcentrationusingensemblelearning-2021","role":"author","urls":{},"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"article","biburl":"https://api.krunk.cn/emil/bib.php","dataSources":["HEdahWqKBpmSGmDwq","N2pLmFa7tezaioGCg","MF5eGzpJnqf6bSAoG","Qrg38B3HrfoKD7g5k","ienufKdnmJs49AsjR","So4gmSWFmbQRNEuFs","ezsmw4w22u9JFLNYJ","CvQYP6Tmpapx74Mgr","RWydLHbBJqgdeh5jr"],"keywords":[],"search_terms":["real","time","prediction","river","chloride","concentration","using","ensemble","learning","zhang","li","zhu","zhang","sekerinski","han","zhou"],"title":"Real-time prediction of river chloride concentration using ensemble learning","year":2021,"downloads":1}