Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data

Chen, Kangyang; Chen, Hexia; Zhou, Chuanlong; Huang, Yi; Xiang-yang, QI; Shen, Ruinan; Liu, Fengrui; Zuo, Min; Zou, Xinyi; Wang, Jinfeng; Zhang, Yan; Chen, Da; Chen, Xingguo; Deng, Yongfeng; Ren, Hongqiang

doi:10.1016/j.watres.2019.115454

Cited by 356 publications

(155 citation statements)

References 27 publications

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“…In recent years, a wide variety of AI models, such as the Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Extreme Learning Machine (ELM) and genetic programming (GP), has been extensively recommended and applied to investigate hotspots in hydrologic research, mainly focusing on rainfall, runoff, reference evapotranspiration, flood, water quality and groundwater level forecast [9][10][11][12][13][14][15]. In terms of groundwater level prediction, the applicability and potential of these AI methods have been confirmed [16].…”

Section: Introductionmentioning

confidence: 99%

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Feng

Wen

et al. 2021

Water

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Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting.

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Section: Introductionmentioning

confidence: 99%

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Feng

Wen

et al. 2021

Water

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show abstract

“…For example, Fijani et al designed a hybrid two-layer decomposition using the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) and the variational mode decomposition (VMD) algorithm coupled with extreme learning machines (ELM) to predict chlorophyll-a (Chl-a) and dissolved oxygen (DO) in a reservoir [3]. Chen et al compared the water quality prediction performances of several machine learning methods using monitoring data from the major rivers and lakes in China from 2012 to 2018 [4]. Lu et al designed two hybrid decision tree-based models to predict the water quality for the most polluted river Tualatin River in Oregon, USA [5].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Sha

Zhang

et al. 2021

Water

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Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

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“…This study indicated that the removal efficiency of many parameters of water quality was not linear with input variables indicating the low values of R 2 of predictive model while some others achieved only 0.5 -0.65 in R 2 . Other comprehensive studies consisting of training and validation, which used and compared ML algorithms for evaluating the effluent concentration J o u r n a l P r e -p r o o f and the water quality have also been launched (Chen et al, 2020, Manu and Thalla, 2017, Wu et al, 2015. The random forest (RF) algorithm was also of interest in given aquatic systems.…”

Section: Introductionmentioning

confidence: 99%

Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: A comparative study and prediction of effluents using machine learning

Nguyen

Peng

et al. 2021

Journal of Hazardous Materials

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Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data

Cited by 356 publications

References 27 publications

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: A comparative study and prediction of effluents using machine learning

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