River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

Asadollah, Seyed Babak Haji Seyed; Sharafati, Ahmad; Motta, Davide; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.jece.2020.104599

Cited by 219 publications

(95 citation statements)

References 64 publications

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“…Advancement of water quality modelling is demonstrated by Asadollah, et al [142] using ensemble learning model. The study combined decision tree (DT) weak learners with classic standalone SVR techniques to improve prediction performance.…”

Section: ) Water Quality Modellingmentioning

confidence: 99%

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

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The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems. INDEX TERMS Mangrove estuarine; pollutant interaction; environmental quality modelling; integrated environmental decision system.

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Section: ) Water Quality Modellingmentioning

confidence: 99%

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

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“…In addition to the classical statistical regression methods, supervised machine learning (SML) approaches such as artificial neural network (ANN), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) have been adopted in many hydrological studies (Suen and Eheart 2003;Asadollahfardi et al 2012;Shamshirband et al 2015;Alrashed et al 2018;Yaseen et al 2018;Sinshaw et al 2019;Haghbin et al 2020). These studies include forecasting nitrate concentration in rivers (Suen and Eheart 2003;Haghbin et al 2020), modelling total phosphorus and total nitrogen in wetlands (Asadollahfardi et al 2010), predicting TDS in rivers (Asadollahfardi et al 2012), estimating the concentration of total nitrogen and total phosphorus in lakes (Sinshaw et al 2019), analysing the thermal behaviour and performance of nano-suspensions in water supply systems (Shamshirband et al 2015;Alrashed et al 2018;Karimipour et al 2019) and predicting water quality parameters including TDS, biochemical oxygen demand (BOD), and chemical oxygen demand (COD) using three new ensemble machine learning models (Asadollah et al 2020;Sharafati et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Modelling of total dissolved solids in water supply systems using regression and supervised machine learning approaches

2021

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Monitoring of water quality through accurate predictions provides adequate information about water management. In the present study, three different modelling approaches: Gaussian process regression (GPR), backpropagation neural network (BPNN) and principal component regression (PCR) models were used to predict the total dissolved solids (TDS) as water quality indicator for the water quality management. The performance of each model was evaluated based on three different sets of inputs from groundwater (GW), surface water (SW) and drinking water (DW). The GPR, BPNN and PCR models used in this study gave an accurate prediction of the observed data (TDS) in GW, SW and DW, with the R2 consistently greater than 0.850. The GPR model gave a better prediction of TDS concentration, with an average R2, MAE and RMSE of 0.987, 4.090 and 7.910, respectively. For the BPNN, an average R2, MAE and RMSE of 0.913, 9.720 and 19.137, respectively, were achieved, while the PCR gave an average R2, MAE and RMSE of 0.888, 11.327 and 25.032, respectively. The performance of each model was assessed using efficiency based indicators such as the Nash and Sutcliffe coefficient of efficiency (ENS) and the index of agreement (d). The GPR, BPNN and PCR models, respectively, gave an ENS of (0.967, 0.915, 0.874) and d of (0.992, 0.977, 0.965). It is understood from this study that advanced machine learning approaches (e.g. GPR and BPNN) are appropriate for the prediction of water quality indices and would be useful for future prediction and management of water quality parameters of various water supply systems in mining communities where artificial intelligence technology is yet to be fully explored.

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“…Afterward, aeration is performed in the diverging portion by changes the supercritical flow to subcritical. The dissolved oxygen (DO) concentration is an influential factor in biological and chemical activities in aquatic ecosystems, such as the self-purification of the rivers (Asadollah et al, 2020;Sharafati et al, 2020a). A minimum level of DO is necessary for the survival of the aquatic life, and thus several problems would be raised for the aquatic life when the DO concentration drops down to 5 mg/l.…”

Section: Introductionmentioning

confidence: 99%

Machine learning model development for predicting aeration efficiency through Parshall flume

Sangeeta

Asadollah

Sharafati

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E 20 ) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model (RMSE testing = 0.002, R 2 testing = 0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.

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River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

Cited by 219 publications

References 64 publications

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Modelling of total dissolved solids in water supply systems using regression and supervised machine learning approaches

Machine learning model development for predicting aeration efficiency through Parshall flume

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