Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques

Ay, Murat; Kişi, Özgür

doi:10.1007/s12205-016-0728-6

Cited by 40 publications

(21 citation statements)

References 29 publications

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“…To facilitate optimization, minimize the impact of different dimensions, and achieve more effective results, all three input variables and the LDO were normalized using Equation (4) [63][64][65]. Different normalization formulas are also used in water quality modeling studies but there are no fixed rules as to which standardization approach should be used in particular circumstances [19,66]. In this study, "a" and "b" were taken as 0.8 and 0.1, respectively.…”

Section: Model Development Applicationsmentioning

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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Section: Model Development Applicationsmentioning

confidence: 99%

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

Nacar

Bayram

BAKİ

et al. 2020

Water

View full text Add to dashboard Cite

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“…The common mechanism models include the BASINS model system [8], the MIKE model system [9], and the QUAL2K model system [10]. However, it is often challenging to simulate the target water quality systems when lacking adequate monitoring data or background information [11]. Consequently, those models are not likely to be able to be generalized without significant parameter adjustment [12].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Trend of Dissolved Oxygen Based on the kPCA-RNN Model

Zhang

Fitch

Thorburn

2020

Water

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Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on the kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN’s recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R 2 scores up to 0.908, 0.823, and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like Feed-forward neural network (FFNN), support vector regression (SVR) and general regression neural network (GRNN), the predictive accuracy of the kPCA-RNN model was at least 8%, 17% and 12% better than the comparative models in these three cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

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“…[2]. DO is a significant parameter for determining water quality and a key point affecting aquatic habitats [3][4][5][6]. High levels of BOD can lead to severe oxygen depletion and affect aquatic habitat [7,8].…”

Section: Introductionmentioning

confidence: 99%

Monitoring and modelling water quality of Loktak Lake catchment

et al. 2019

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Loktak Lake is an internationally important, Ramsar designated, fresh water wetland system in the state of Manipur, India. The lake has also been listed under Montreux Record on account of the ecological modifications that the lake system has witnessed over time. Discharges from nine rivers namely Khuga, Thongjaorok, Awang Khujairok, Nambol, Nambul, Imphal, Kongba, Iril, and Thoubal have a great impact on the habitats and the overall ecological status of the lake. Monitoring of water quality at the catchment scale can be considered as an essential step towards the eventual goal to design effective conservation and management practices for the entire Loktak Lake ecosystem. This article presents the status of nine rivers draining into the Loktak Lake and correlation with land use patterns which can be used as support for making sound decisions regarding the management of the lake ecosystems. Flows were modelled using a combination of soil and water assessment tool (SWAT) and MIKE SHE, abbreviated as hybrid SHE-SWAT. Water quality models were established using MIKE 11 ECO Lab. Water quality parameters such as biological oxygen demand, dissolved oxygen and water temperature were simulated. Water quality models were calibrated using available measured water quality data procured from State Pollution Control Board and validated using observed water quality collected during the field study.

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Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques

Cited by 40 publications

References 29 publications

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

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

Predicting the Trend of Dissolved Oxygen Based on the kPCA-RNN Model

Monitoring and modelling water quality of Loktak Lake catchment

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