Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach

Elkiran, Gözen; Nourani, Vahid; Abba, S.I.

doi:10.1016/j.jhydrol.2019.123962

Cited by 158 publications

(52 citation statements)

References 48 publications

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“…They are based on an understanding of the biological nervous system Solving the nonlinear problems [19,23,30,[32][33][34][35] TDNNs They are based on the structure of MLPs Using time delay cells to deal with the dynamic nature of sample data [36] RBFNNs The structure of RBFNNs is similar to the MLPs The radial basis activation function is in the hidden layer…”

Section: Mlpsmentioning

confidence: 99%

“…The core of the technique intensive methods is to develop a modeling framework that is able to take advantage of different technologies. Methods that combine ensemble approaches [32] or time series models that remove trends or periodicities like Autoregressive Integrated Moving Average-Radial Basis Function neural networks (ARIMA-RBFNNs) [70] or ARIMA-ANN [71] are technique-intensive methods. In this review, data-intensive approaches are to combine different technologies to preprocess the data.…”

Section: Hybrid Architecturesmentioning

confidence: 99%

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A Review of the Artificial Neural Network Models for Water Quality Prediction

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Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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Section: Mlpsmentioning

confidence: 99%

Section: Hybrid Architecturesmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

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“…The main target of the ensemble is to produce higher accuracy and reliable estimates than could be achieved through a single model [63]. As reported by Kiran and Ravi, [44], Nourani et al [48], there are two ensemble techniques: (1) linear ensemble method, which includes linear ensembles by simple averaging, weighted averaging and weighted median; and (2) nonlinear ensemble method, which involve the use of black-box model as nonlinear kernels to obtain an ensemble output. Other researchers have categorized the ELT into two, namely homogeneous and heterogeneous ensembles; when ELT comprised of the same learning algorithm (e.g.…”

Section: Ensemble Learning Technique (Elt)mentioning

confidence: 99%

“…Except for TS, all the WQ variables show excellent inverse relationship with the DO. Even though studies such as [45], [48], [49], [61], [81] have criticized the classical linear input variable selection and recommended the use of nonlinear approaches; they are applicable for input selection and the determination of linear patterns between the variables. The observed WQ parameters were analysed, and the statistical overview of the data was obtained as presented in Table 1.…”

Section: Figure 4 Pearson's Correlations Coefficients Among the Obsementioning

confidence: 99%

“…The outcomes indicated that the ensemble model has better prediction accuracy. This technique has been also applied in various fields of hydro-environmental engineering, such as precipitation [46], earth-fill dam seepage analysis [41], evapotranspiration [47], and River WQ [48]. One of the factors affecting the accuracy of the models is the model input determination, which depends on the identification of the AI-based models, and others include model configuration, prediction horizon, etc.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration

et al. 2020

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The reliable prediction of dissolved oxygen concentration (DO) is significantly crucial for protecting the health of the aquatic ecosystem. The current research employed four different single AI-based models, namely long short-term memory neural network (LSTM), extreme learning machine (ELM), Hammerstein-Weiner (HW) and general regression neural network (GRNN) for modeling the DO concentration of Kinta River, Malaysia using available water quality (WQ) parameters. Afterwards, the first scenario used four different ensemble techniques (ET). Two linear, i.e. simple averaging ensemble (SAE) and weighted averaging ensemble (WAE) and two nonlinear namely; backpropagation neural network ensemble (BPNN-E) and HW ensemble (HW-E). The second scenario employed a hybrid random forest (RF) ensemble in order to enhance the prediction accuracy of the single models. The WQ parameters were subjected to a different pre-analysis test to ascertain their stability. The four-model combinations are generated using the nonlinear sensitivity input selection approach. The modeling performance was assessed using the statistical measures of Nash-Sutcliffe coefficient efficiency (NSE), Willmott's index of agreement (WI), root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE) and correlation coefficient (CC). The results of the single AI-based models demonstrated that HW (M3) served as the best model for predicting DO concentration. For ensemble results, BPNN-E (WI=0.9764) was superior to the other three ET with average decreased of more than 2% with regards to MAE. Investigation on the hybrid RF ensemble demonstrated the reliable accuracy for all the hybrid models with better predictive skill shown by the HW-RF (CC=0.981) ensemble. The overall results verified the promising impact of HW-M3, ET and hybrid RF ensemble for the prediction of the DO concentration in the Kinta River, Malaysia.

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