Ensemble data assimilation methods for improving river water quality forecasting accuracy

Loos, Sibren; Shin, Chang Min; Sumihar, Julius; Kim, Kyunghyun; Cho, Jaegab; Weerts, Albrecht

doi:10.1016/j.watres.2019.115343

Cited by 52 publications

(33 citation statements)

References 31 publications

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“…In the data assimilation and forecasting steps of the daily workflow (Figure 1), we used N = 441 ensemble members. Even though fewer ensemble members have been shown to be effective when applying the EnKF in other studies (e.g., Loos et al, 2020), our ensemble size was selected to adequately sample forecasted meteorology drivers to include uncertainty from both NOAA GEFS and its statistical downscaling. In the forecasting step for this application (Figure 1), we generated 21 members of our downscaling ensemble for each of the 21 NOAA GEFS ensemble members (following Supporting Information S3, Supporting Information Table S2, and Supporting Information Table S3), resulting in a total ensemble size of N = 441.…”

Section: Methodsmentioning

confidence: 99%

A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

Thomas

Figueiredo

Daneshmand

et al. 2020

Water Resources Research

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Section: Methodsmentioning

confidence: 99%

A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

Thomas

Figueiredo

Daneshmand

et al. 2020

Water Resources Research

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“…Thus, ensemble machine learning is applied in different field of science, engineering, social and management science and health science etc. In chemistry, it can be used in synthesis, spectroscopy, colour, polymer and chromatography [22][23][24][25]. Ensemble technique is collectively considered as a class of machine learning, which deals with compound heterogeneous and homogenous models [22].…”

Section: Ensemble Techniquesmentioning

confidence: 99%

Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach

et al. 2020

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This work employs the application of three artificial intelligence (AI) techniques namely; support vector machine (SVM), Hammerstein-Wiener (HW) and multi-layer perceptron (MLP) for predicting the qualitative properties of an anti-Alzheimer agent using high-pressure liquid chromatography technique. The mobile phase (inform of acetonitrile and trifluoroacetic acid) and the column temperature was used as the predictors in modelling the maximum retention time (tR-max) and resolution (Resol.) as the output variables of the analyte. The measured and predicted values were checked using three performance indices including; Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC) as the goodness of fits and a statistical error inform of root-mean-square error (RMSE). The results obtained demonstrated the promising ability of AI-based models in modelling the qualitative properties of the anti-Alzheimer agent. Observation of different outputs of the AI-based models at various time intervals showed the necessity of ensembling the outputs of the AI-based models. Therefore, simple average ensemble and support vector machine ensemble (SVM-E) were employed to enhance the performance skills of the simple models. The comparative performance of SVM-E inform of NSE indicated its ability in boosting and enhancing the performance skills of the single models SVM, MLP and HW models up to 5, 13 and 20% respectively in the testing stage for modelling tR-max.

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“…For the purpose of this research, the employed data‐driven intelligence, ANN, ANFIS, MLR models, and novel ensemble techniques, and a brief description of the mathematical concept for each of these models as well as the related citations and equations are provided in the Supporting Information Appendix (A1–A4) in more detail [28‐35].…”

Section: Methodsmentioning

confidence: 99%

Chemometrics‐based models hyphenated with ensemble machine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high‐performance liquid chromatography

Usman

Işık

Abba

et al. 2021

J of Separation Science

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In this research, two nonlinear models, namely; adaptive neuro‐fuzzy inference system and feed‐forward neural network and a classical linear model were employed for the prediction of retention time of isoquercitrin in Coriander sativum L. using the high‐performance liquid chromatography technique. The prediction employed the use of composition of mobile phase and pH as the corresponding input parameters. The performance indices of the models were evaluated using root mean square error, determination co‐efficient, and correlation co‐efficient. The results obtained from the simple models showed that subclustering‐adaptive‐neuro fuzzy inference system gave the best results in both the training and testing phases and boosted the performance accuracy of the simple models. The overall comparison of the results showed that subclustering‐adaptive‐neuro fuzzy inference system ensemble demonstrated outstanding performance and increased the accuracy of the single models and ensemble models in the testing phase, up to 35% and 3%, respectively.

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Ensemble data assimilation methods for improving river water quality forecasting accuracy

Cited by 52 publications

References 31 publications

A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach

Chemometrics‐based models hyphenated with ensemble machine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high‐performance liquid chromatography

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