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Yang R, Liu H, Li Y. Quantifying uncertainty of marine water quality forecasts for environmental management using a dynamic multi-factor analysis and multi-resolution ensemble approach. CHEMOSPHERE 2023; 331:138831. [PMID: 37137396 DOI: 10.1016/j.chemosphere.2023.138831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/25/2023] [Accepted: 04/30/2023] [Indexed: 05/05/2023]
Abstract
Unpredictable climate change and human activities pose enormous challenges to assessing the water quality components in the marine environment. Accurately quantifying the uncertainty of water quality forecasts can help decision-makers implement more scientific water pollution management strategies. This work introduces a new method of uncertainty quantification driven by point prediction for solving the engineering problem of water quality forecasting under the influence of complex environmental factors. The constructed multi-factor correlation analysis system can dynamically adjust the combined weight of environmental indicators according to the performance, thereby increasing the interpretability of data fusion. The designed singular spectrum analysis is utilized to reduce the volatility of the original water quality data. The real-time decomposition technique cleverly avoids the problem of data leakage. The multi-resolution-multi-objective optimization ensemble method is adopted to absorb the characteristics of different resolution data, so as to mine deeper potential information. Experimental studies are conducted using 6 actual water quality high-resolution signals with 21,600 sampling points from the Pacific islands and corresponding low-resolution signals with 900 sampling points, including temperature, salinity, turbidity, chlorophyll, dissolved oxygen, and oxygen saturation. The results illustrate that the model is superior to the existing model in quantifying the uncertainty of water quality prediction.
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Affiliation(s)
- Rui Yang
- Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha, 410075, Hunan, China
| | - Hui Liu
- Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha, 410075, Hunan, China.
| | - Yanfei Li
- School of Mechatronic Engineering, Hunan Agricultural University, Changsha, 410128, Hunan, China
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2
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Fu S, Gao X, Li B, Xue B, Jia X, Huang Z, Zhang G, Huang X. Two Outlier-Sensitive Measures for Semi-supervised Dynamic Ensemble Anomaly Detection Models. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11017-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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3
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Zhang Q, Li Z, Zhu L, Zhang F, Sekerinski E, Han JC, Zhou Y. Real-time prediction of river chloride concentration using ensemble learning. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 291:118116. [PMID: 34537597 DOI: 10.1016/j.envpol.2021.118116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 08/27/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
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.
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Affiliation(s)
- Qianqian Zhang
- Chengdu University of Information Technology, Chengdu, 610225, China; Department of Civil Engineering, McMaster University, Hamilton, Ontario, L8S 4L8, Canada
| | - Zhong Li
- Department of Civil Engineering, McMaster University, Hamilton, Ontario, L8S 4L8, Canada.
| | - Lu Zhu
- Department of Chemical Engineering, McMaster University, Hamilton, Ontario, L8S 4L8, Canada
| | - Fei Zhang
- SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; CAS Center for Excellence in Quaternary Science and Global Change, Xi'an, 710061, China
| | - Emil Sekerinski
- Department of Computing and Software, McMaster University, Hamilton, Ontario, L8S 4L8, Canada
| | - Jing-Cheng Han
- Water Science and Environmental Engineering Research Center, College of Chemical and Environmental Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Yang Zhou
- Water Science and Environmental Engineering Research Center, College of Chemical and Environmental Engineering, Shenzhen University, Shenzhen, 518060, China
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Dai M, Yang F, Zhang Z, Liu G, Feng X, Hou J. Prediction of liquid ammonia yield using a novel deep learning‐based heterogeneous pruning ensemble model. ASIA-PAC J CHEM ENG 2020. [DOI: 10.1002/apj.2408] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Min Dai
- School of Chemical Engineering and TechnologyXi'an Jiaotong University Xi'an China
| | - Fusheng Yang
- School of Chemical Engineering and TechnologyXi'an Jiaotong University Xi'an China
| | - Zaoxiao Zhang
- School of Chemical Engineering and TechnologyXi'an Jiaotong University Xi'an China
| | - Guilian Liu
- School of Chemical Engineering and TechnologyXi'an Jiaotong University Xi'an China
| | - Xiao Feng
- School of Chemical Engineering and TechnologyXi'an Jiaotong University Xi'an China
| | - Jianmin Hou
- Xi'an Centum Automatic Control Inc. Xi'an China
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Deng W, Wang G. A novel water quality data analysis framework based on time-series data mining. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2017; 196:365-375. [PMID: 28324852 DOI: 10.1016/j.jenvman.2017.03.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 02/23/2017] [Accepted: 03/08/2017] [Indexed: 06/06/2023]
Abstract
The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data.
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Affiliation(s)
- Weihui Deng
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Guoyin Wang
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
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Kew W, Mitchell JBO. Greedy and Linear Ensembles of Machine Learning Methods Outperform Single Approaches for QSPR Regression Problems. Mol Inform 2015; 34:634-47. [DOI: 10.1002/minf.201400122] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 01/20/2015] [Indexed: 12/20/2022]
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Roy M, Ghosh S, Ghosh A. A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.01.037] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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11
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Liu S, Tai H, Ding Q, Li D, Xu L, Wei Y. A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. ACTA ACUST UNITED AC 2013. [DOI: 10.1016/j.mcm.2011.11.021] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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García S, Fernández A, Luengo J, Herrera F. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci (N Y) 2010. [DOI: 10.1016/j.ins.2009.12.010] [Citation(s) in RCA: 906] [Impact Index Per Article: 64.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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