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(searched for: doi:10.1680/jwama.17.00079)
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Mohammad Kazem Ghorbani, , ,
Proceedings of the Institution of Civil Engineers - Water Management pp 1-16; https://doi.org/10.1680/jwama.20.00081

Abstract:
A fuzzy multi-objective optimisation model was investigated for water quality management in a river under uncertain conditions. The study considered the National Sanitation Foundation Water Quality Index as one of the model's objective functions based on fuzzy set theory to deal with multiple pollutants simultaneously. This made it possible to investigate the overall effect of uncertainties on simultaneous changes. Another objective function was the total treatment costs for wastewater discharged into the river. A water quality simulation model and a non-dominated archiving ant colony optimisation algorithm were used to determine the values of water quality parameters and the model's optimal solutions, respectively. Furthermore, a simulation-optimisation approach was adopted for facilitating the problem-solving process and applied to a hypothetical case study resembling a river system in Iran. The results show that the proposed model significantly reduced the total wastewater treatment costs compared to the similar single-objective model with a more cautious and cost-effective approach. Although the treatment costs were increased relatively compared to the similar deterministic model, it took a more possible approach by considering the uncertainties associated with the objectives. Furthermore, the model ccould be easily adapted for other river systems with suitable modifications.
Published: 23 July 2021
by MDPI
Abstract:
Numerous models have been proposed in the past to predict the maximum scour depth around bridge piers. These studies have all focused on the different parameters that could affect the maximum scour depth and the model accuracy. One of the main parameters individuated is the critical velocity of the approaching flow. The present study aimed at investigating the effect of different equations to determine the critical flow velocity on the accuracy of models for estimating the maximum scour depth around bridge piers. Here, 10 scour depth estimation equations, which include the critical flow velocity as one of the influencing parameters, and 8 critical velocity estimation equations were examined, for a total combination of 80 hybrid models. In addition, a sensitivity analysis of the selected scour depth equations to the critical velocity was investigated. The results of the selected models were compared with experimental data, and the best hybrid models were identified using statistical indicators. The accuracy of the best models, including YJAF-VRAD, YJAF-VARN, and YJAI-VRAD models, was also evaluated using field data available in the literature. Finally, correction factors were implied to the selected models to increase their accuracy in predicting the maximum scour depth.
Environmental Science and Pollution Research, Volume 28, pp 35971-35990; https://doi.org/10.1007/s11356-021-12651-0

Abstract:
The longitudinal dispersion coefficient (LDC) of river pollutants is considered as one of the prominent water quality parameters. In this regard, numerous research studies have been conducted in recent years, and various equations have been extracted based on hydrodynamic and geometric elements. LDC’s estimated values obtained using different equations reveal a significant uncertainty due to this phenomenon’s complexity. In the present study, the crow search algorithm (CSA) is applied to increase the equation’s precision by employing evolutionary polynomial regression (EPR) to model an extensive amount of geometrical and hydraulic data. The results indicate that the CSA improves the performance of EPR in terms of R 2 (0.8), Willmott’s index of agreement (0.93), Nash–Sutcliffe efficiency (0.77), and overall index (0.84). In addition, the reliability analysis of the proposed equation (i.e., CSA) reduced the failure probability (P f) when the value of the failure state containing 50 to 600 m2/s is increasing for the P f determination using the Monte Carlo simulation. The best-fitted function for correct failure probability prediction was the power with R 2 = 0.98 compared with linear and exponential functions.
Leonardo Goliatt, Sadeq Oleiwi Sulaiman, Khaled Mohamed Khedher, Aitazaz Ahsan Farooque,
Engineering Applications of Computational Fluid Mechanics, Volume 15, pp 1298-1320; https://doi.org/10.1080/19942060.2021.1972043

Abstract:
Among several indicators for river engineering sustainability, the longitudinal dispersion coefficient (Kx) is the main parameter that defines the transport of pollutants in natural streams. Accurate estimation of Kx has been challenging for hydrologists due to the high stochasticity and non-linearity of this hydraulic-environmental parameter. This study presents a new hybrid machine learning (ML) model integrating a Gaussian Process Regression (GPR) and an evolutionary feature selection (FS) approach (i.e. Covariance Matrix Adaptation Evolution Strategy (CMAES)) to estimate Kx in natural streams. The dataset consists of geometric and hydraulic river system parameters from 29 streams in the United States. The modeling results showed that the proposed model outperformed other models in the literature, producing more stable and accurate estimations. The FS approach evidenced the significance of the cross-sectional average flow velocity (U), channel width (B), and channel sinuosity σ to estimate the dispersion coefficient. In quantitative terms, the integrated GPR model with feature selection approach attained the minimum root mean square error (RMSE=48.67) and maximum coefficient of determination (R2=0.95). The proposed hybrid evolutionary ML model arises as robust, flexible and reliable alternative computer aid technology for predicting the longitudinal dispersion coefficient in natural streams.
Daniel Valero
Proceedings of the Institution of Civil Engineers - Water Management, Volume 172, pp 217-217; https://doi.org/10.1680/jwama.2019.172.5.217

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