Estimation of re-aeration coefficient using MLR for modelling water quality of rivers in urban environment

Arora, Sameer; Keshari, Ashok K.

doi:10.1016/j.gsd.2017.11.006

Cited by 28 publications

(14 citation statements)

References 23 publications

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“…Thus, in this study it can also be recommended to study temporal variations as well as spatial variations in water quality in order to assess the water quality of the Riva River Basin. This situation is similar to other water quality studies [21][22][23][24][25][26][27][28]. In the studies evaluating the spatial-temporal changes of the water quality parameters in the river basin, it was found that the predictions in the spatial analyzes as well as the predictions in the temporal analyzes were correct [19].…”

Section: Discussionsupporting

confidence: 87%

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Prediction of Water Quality in Riva River Watershed

Öz

Topal

Uzun

2019

Ecological Chemistry and Engineering S

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The Riva River is a water basin located within the borders of Istanbul in the Marmara Region (Turkey) in the south-north direction. Water samples were taken for the 35 km drainage area of the Riva River Basin before the river flows into the Black Sea at 4 stations on the Riva River every month and analyses were carried out. Changes were observed in the quality of water from upstream to downstream. For this purpose, the spatial and temporal variations of water quality were investigated using 13 water quality variables with the ANOVA test. It was observed that COD, DO, S and BOD were important in determining the spatial variation. On the other hand, it was found out that all the variables were effective in determining the temporal variation. Moreover, the correlation analysis which was carried out in order to assess the relations between water quality variables showed that the variables of BOD-COD, BOD-EC, COD-EC, BOD-T and COD-T were correlated and the regression analysis showed that COD, TKN and NH4-N explained BOD and BOD, NH4-N, T and TSS explained COD by approximately 80 %. Consequently, the Artificial Neural Network (ANN), Decision Tree and Logistic Regression models were developed using the data of training set in order to predict the water quality classes of the variables of COD, BOD and NH4-N. Quality classes were predicted for the variables by inputting the data of testing set into the developed models. According to these results, it was seen that the ANN was the best prediction model for COD, the Decision Tree for BOD and the ANN and Decision Tree for NH4-N.

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Section: Discussionsupporting

confidence: 87%

“…As the BOD increases, the saturation level of DO in the river reaches the minimum. Therefore, BOD is important among water quality parameters [26]. According to other studies, COD and DO have excellent performance in reflecting the water quality of the basin [27].…”

Section: Analysis Of the Differences Of Water Quality Parameters Withmentioning

confidence: 95%

Prediction of Water Quality in Riva River Watershed

Öz

Topal

Uzun

2019

Ecological Chemistry and Engineering S

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“…Most statistical calculations are performed using linear regression models, which have been frequently applied in different fields [18][19][20]. Almost every discipline utilizes regression analyses as a basis for comparing improved models [21][22][23].…”

Section: Multiple Linear Regression Modelmentioning

confidence: 99%

Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

Yin

Jia

et al. 2018

Water

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Water-energy nexus has been a popular topic of rese arch in recent years. The relationships between the demand for water resources and energy are intense and closely connected in urban areas. The primary, secondary, and tertiary industry gross domestic product (GDP), the total population, the urban population, annual precipitation, agricultural and industrial water consumption, tap water supply, the total discharge of industrial wastewater, the daily sewage treatment capacity, total and domestic electricity consumption, and the consumption of coal in industrial enterprises above the designed size were chosen as input indicators. A feedforward artificial neural network model (ANN) based on a back-propagation algorithm with two hidden layers was constructed to combine urban water resources with energy demand. This model used historical data from 1991 to 2016 from Wuxi City, eastern China. Furthermore, a multiple linear regression model (MLR) was introduced for comparison with the ANN. The results show the following: (a) The mean relative error values of the forecast and historical urban water-energy demands are 1.58 % and 2.71%, respectively; (b) The predicted water-energy demand value for 2020 is 4.843 billion cubic meters and 47.561 million tons of standard coal equivalent; (c) The predicted water-energy demand value in the year 2030 is 5.887 billion cubic meters and 60.355 million tons of standard coal equivalent; (d) Compared with the MLR, the ANN performed better in fitting training data, which achieved a more satisfactory accuracy and may provide a reference for urban water-energy supply planning decisions.

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“…Both the hydraulic and water quality parameters affect water quality. Low velocity and discharge in the river reduces the waste load‐carrying capacity of streams (Arora and Keshari, 2018) whereas, discharge of organic impurities, metals and chemical compounds alters the physical, chemical and biological properties of water body (Swaroop, 1985; Jha et al ., 2001). Along with the anthropogenic sources, natural processes such as seasonal variations, precipitation and the exchange of water under the hyporheic zone also alter the state of the river tremendously (Merill and Tonjes, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the researchers have either used the fundamental equation of Streeter and Phelps (1925) or modified this equation to estimate the dissolved oxygen balance in their study area. Along with the physio‐chemical factors, the self‐purification capacity of the river is also greatly affected by the hydraulic parameters such as flow available in the river, velocity, depth and channel cross‐sectional area (Arora and Keshari, 2018). Several predictive empirical re‐aeration equations are available in the literature developed by various researchers (O'Connor and Dobbins, 1956; Churchill et al ., 1962; Owens et al ., 1964; Langbein and Durum, 1967; Isaacs et al ., 1969; Thackston and Krenkel, 1969; Bansal, 1973; Moog and Jirka, 1998; Jha et al ., 2001; Omole and Longe, 2012).…”

Section: Introductionmentioning

confidence: 99%

Development of a re‐aeration equation for the simulation of dissolved oxygen in degraded streams

Arora

Keshari

2020

Water & Environment J

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Discharge of domestic and industrial wastewater into the water bodies is the prime source of degradation of the freshwater ecosystem. Re‐aeration of the river allows the rejuvenation of river water quality by absorbing oxygen from the atmosphere at the air‐water interface. An accurate estimation of the re‐aeration coefficient helps to determine the assimilative capacity of streams and wastewater management also. This paper aims to develop the re‐aeration equation and its validation by modelling dissolved oxygen of the Yamuna River. Predictive re‐aeration equations are used to identify their applicability to the study area and the new equation is designed using multivariate statistical regression techniques. QUAL2Kw model is used to validate the equation by modelling dissolved oxygen. Results indicate that the developed equation performs better than the predictive re‐aeration equations for the prediction of water quality.

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Estimation of re-aeration coefficient using MLR for modelling water quality of rivers in urban environment

Cited by 28 publications

References 23 publications

Prediction of Water Quality in Riva River Watershed

Prediction of Water Quality in Riva River Watershed

Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

Development of a re‐aeration equation for the simulation of dissolved oxygen in degraded streams

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