Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA

Heddam, Salim

doi:10.1007/s11356-014-2842-7

Cited by 57 publications

(15 citation statements)

References 61 publications

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“…Subsequently, the dynamic evolving neural-fuzzy inference system (DENFIS) model which uses the evolving clustering method (ECM) for clustering fuzzy rules is described. It is worth noting that this method has been able to indicate very suitable performance in modelling complex problems [44,45]. Eventually, the MARS method, which is capable of generating an efficient model with the capability of modelling non-linear and complex problems using the concepts of linear regression, is described.…”

Section: Used Methodsmentioning

confidence: 99%

Prediction of Suspended Sediment Load Using Data-Driven Models

Adnan

Liang

El-Shafie

et al. 2019

Water

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Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China-Guangyuan and Beibei-were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE). The data period covers 01/04/2007-12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data.

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Section: Used Methodsmentioning

confidence: 99%

Prediction of Suspended Sediment Load Using Data-Driven Models

Adnan

Liang

El-Shafie

et al. 2019

Water

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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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“…A RBFNN model was developed to predict the dissolved oxygen from biochemical oxygen demand (BOD) and chemical oxygen demand (COD) in the Surma River [ 13 ]. Samira and Salim developed an adaptive neural based fuzzy inference system [ 14 ] and dynamic evolving neural-fuzzy inference system [ 15 ] for modeling river dissolved oxygen concentration. Recently, a new extreme learning machine model was applied to predict dissolved oxygen concentration with and without water quality variables as predictors.…”

Section: Introductionmentioning

confidence: 99%

A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture

Chen

Cheng

et al. 2018

PLoS ONE

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A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies.

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Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA

Cited by 57 publications

References 61 publications

Prediction of Suspended Sediment Load Using Data-Driven Models

Prediction of Suspended Sediment Load Using Data-Driven Models

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

A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture

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