Estimation of dissolved oxygen in riverine ecosystems: Comparison of differently optimized neural networks

Csábrági, Anita; Molnár, Sándor; Tanos, Péter; Kovács, József; Molnár, Márk; Szabó, István; Hatvani, István Gábor

doi:10.1016/j.ecoleng.2019.07.023

Cited by 39 publications

(20 citation statements)

References 46 publications

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“…Csábrági, et al [113] showed the appropriate efficiency of three conventional notions of artificial neural networks (ANNs) by the names multilayer perceptron (MLP), radial basis function (RBF), and general regression neural network (GRNN) for this purpose. Similar efforts can be found in [114,115]. Heddam [116] introduced a new ANN-based model, namely evolving fuzzy neural network as a capable approach for the DO simulation in the river ecosystem.…”

Section: Introductionmentioning

confidence: 87%

Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen

Moayedi¹,

Mosavi²

2021

Preprint

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The great importance of estimating dissolved oxygen (DO) dictates utilizing proper evaluative models. In this work, a multi-layer perceptron (MLP) network is trained by three capable metaheuristic algorithms, namely multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE) for predicting the DO using the data of the Klamath River Station, Oregon, US. The records (DO, water temperature, pH, and specific conductance) belonging to the water years 2015 - 2018 (USGS) are used for pattern analysis. The results of this process showed that all three hybrid models could properly infer the DO behavior. However, the BHA and SCE accomplished this task by simpler configurations. Next, the generalization ability of the developed patterns is tested using the data of the 2019 water year. Referring to the calculated mean absolute errors of 1.0161, 1.1997, and 1.0122, as well as Pearson correlation coefficients of 0.8741, 0.8453, and 0.8775, the MLPs trained by the MVO and SCE perform better than the BHA. Therefore, these two hybrids (i.e., the MLP-MVO and MLP-SCE) can be satisfactorily used for future applications.

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Section: Introductionmentioning

confidence: 87%

Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen

Moayedi¹,

Mosavi²

2021

Preprint

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“…Results demonstrated the effectiveness of the proposed methods. Researchers tended to divide the training set data into 70% to 90% of the total data [39,42,49,52,72,[120][121][122][123][124][125][126][127]. Iglesias et al [35] divided the data into training (90%) and testing sets (10%).…”

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

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

et al. 2020

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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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“…These variables were flow rate (Q), WT, pH, EC, specific conductivity (SC), water depth (WD), total solids (TS), total alkalinity (TA), water hardness (WH), air temperature (AT), nitrite ion (NO 2 − ), nitrate ion (NO 3 − ), ammonium ion (NH 4 + ), phosphate ion (PO 4 3− ), total phosphorus (TP), chemical oxygen demand (COD), sulfate ion (SO 4 2− ), sodium ion (Na + ), potassium ion (K + ), calcium ion (Ca 2+ ), chloride ion (Cl − ), and biochemical oxygen demand (BOD). Taking into account the literature review [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48], the WT, the EC, and the pH (which are most effective in modeling studies) were selected as the independent variables.…”

Section: Modeling Variablesmentioning

confidence: 99%

Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

Nacar

Bayram

BAKİ

et al. 2020

Water

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The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching–learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables.

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Estimation of dissolved oxygen in riverine ecosystems: Comparison of differently optimized neural networks

Cited by 39 publications

References 46 publications

Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen

Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen

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

Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

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