Ahmed Mohamed AbdelSattar

With constrained Nile water and the cruel drop in per capita freshwater share in Egypt, soil aquifer treatment (SAT) appears an attractive, low-cost, unconventional water resource that is environmentally friendly and relatively easy to operate. Nevertheless, organic micropollutants (OMPs) in treated wastewater pose environmental and health risks if not properly attenuated through vadose zone infiltration. Thus, determination of OMP removal in a SAT system is important for sustainable groundwater management. In this study, a new, simple equation for the prediction of organic micropollutant removal in SAT systems was developed using gene expression programming (GEP). A wide range of 15 OMPs and aquifer conditions were examined along with various real operational aspects of SAT systems, including hydraulic loading rate and dry/wet ratio. The effect of spatial heterogeneity on saturated hydraulic conductivity was considered. Developed GEP models had an average coefficient of determination R2 of 0.92. Monte Carlo simulation (MCS) with 50,000 realizations was used to propagate uncertainty in SAT parameters in order to generate stochastic inputs for the GEP model. It was found that the removal of OMPs in SAT systems is mostly affected by biodegradation rate and soil-saturated hydraulic conductivity, in addition to dry/wet ratio. Finally,the developed GEP models were applied to enhance the criteria for selecting potential sites for SAT systems in Egypt considering OMP. It was shown that a SAT system would perform well at three sites, with a OMP removal efficiency reaching 100%, whereas it would have a removal of only 50% in the other two sites. Uncertainties in predictions were quantified with an average value of 35%. The developed GEP models can serve as basis for preliminary SAT site selection and design, and can substitute for complex commercial modeling software, especially for practitioners and decision makers in feasibility studies. However, for SAT implementation in a selected location, results can be confirmed only through field column tests.

Stepped weir is a commonly used hydraulic structure in water treatment plants to enhance the air-water transfer of oxygen or nitrogen and volatile organic components. The flow regimes on stepped weir are classified into nappe, transition and skimming flow. This study presents the novel application of artificial intelligence methods to evaluate the aeration efficiency over stepped weir for the three flow regimes. Two methods were adopted in this study, namely, the evolutionary polynomial regression (EPR) and the M5 model tree (M5 MT). A total of 151 laboratory experimental data sets were collected from the literature to train and test the artificial intelligence models. The Mallow's coefficient CP was used to determine the effective variables affecting aeration efficiency. It was found that weir steps number, slope, the flow Reynolds number, and the ratio of the critical flow depth to the step height are the most important variables providing the lowest Cp. Both the EPR and M5 MT methods provided satisfactory predictions for the aeration efficiency. The two methods have high values of correlation coefficient R> 0.93 and low values for the root mean square error RMSE< 0.052 and relative mean absolute error RMAE< 0.065. However, the EPR method has an advantage over the M5 MT method that it provides one equation for each regime, while the M5 MT method provides a number of equations for each regime. This will make the equations of the EPR method more attractive to the practitioners compared to the equations of the M5 MT method. It was found that the equations obtained from artificial intelligence methods in this study perform better than the currently existing equations in the litrature obtained from regressive methods.

A novel failure rate prediction model is developed by the extreme learning machine (ELM) to provide key information needed for optimum ongoing maintenance/rehabilitation of a water network, meaning the estimated times for the next failures of individual pipes within the network. The developed ELM model is trained using more than 9500 instances of pipe failure in the Greater Toronto Area, Canada from 1920 to 2005 with pipe attributes as inputs, including pipe length, diameter, material, and previously recorded failures. The models show recent, extensive usage of pipe coating with cement mortar and cathodic protection has significantly increased their lifespan. The predictive model includes the pipe protection method as pipe attributes and can reflect in its predictions, the effect of different pipe protection methods on the expected time to the next pipe failure. The developed ELM has a superior prediction accuracy relative to other available machine learning algorithms such as feed-forward artificial neural network that is trained by backpropagation, support vector regression, and non-linear regression. The utility of the models provides useful inputs when planning and budgeting for watermain inspection, maintenance, and rehabilitation.

Rapid population growth and mass migration from rural to urban centers have contributed to a new era of water sacristy, and a significant drop in per capita freshwater availability, resulting in the reuse of wastewater emerging as a viable alternative. The reuse of wastewater after treatment using the soil aquifer treatment (SAT) has recently gained popularity due to low operating/maintenance cost of the method. However, the presence of organic micropollutants (OMPs) may present a health risk if the SAT is not adequately designed to ensure required attenuation of the OMPs. An important aspect of the design of the SAT system is the large degree of natural variability in the OMP concentrations/loads in the wastewater and the uncertainty associated with the current methods for calculation of the removal efficiency of the SAT for the OMPs. This study presents a novel model for more accurate prediction of the removal efficiency of the SAT system for the OMPs and the fate of the OMPs trapped within the vadose zone. A large data set is compiled covering a broad range of aquifer conditions, and the SAT system parameters, including hydraulic loading rate and dry/wet ratio. This study suggests that removal of OMPs in SAT systems is most affected by biodegradation rate and soil saturated hydraulic conductivity, in addition to dry to wet ratio. This conclusion is reached by the application of the developed prediction model using data sets from the case study SAT systems in Egypt.

Open channel levees are used extensively in hydraulic and environmental engineering applications to protect the surrounding area from inundation. However, levees may fail to produce an unsteady flow that is inherently three dimensional. Such a failure may lead to a destructive change in morphology of the river channel and valley. To avoid such a situation arising, hydraulic laboratory modeling was performed on an open channel levee breach model capturing velocity, in x, y and z plans, at selected locations in the breach. Sandbags of various shapes and sizes are tested for incipient motion by the breach flow. We found that a prism sandbag has a better hydrodynamic characteristic and more stability than spherical bags with the same weight. Experimental results are then used to evaluate existing empirical equations and to develop more accurate equations for predicting critical flow velocity at the initial stage of sandbag motion. Results showed the superior predictions a few of the equations could be considered with an uncertainty range of ±10%. These equations explained the initial failed attempts of the United States Army Corps of Engineers (USACE) for breach closure of the case study, and confirmed the experimental results are simulating the case study of breach closure.

{Grade-control structures (GCS) are commonly used to protect fish habitat by preventing excessive river-bed degradation in mountain streams. However, flow over the GCS can cause localized scour immediately downstream of the weir. This paper aims to develop more accurate models for prediction of the maximum scour depth downstream of GCS, using a more extensive dataset and evolutionary gene expression programming (GEP). Three GEP models are developed relating maximum scour depth and various control variables. The developed models had the lowest error compared to available models. A parametric analysis is performed for further verification of the developed GEP model. The results indicate that the proposed relations are simple and can more accurately predict the scour depth downstream GCS.}

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The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for automatic generation of computer programs. In recent decades, GP has been frequently applied on various kind of engineering problems and undergone speedy advancements. A number of studies have demonstrated the advantage of GP to solve many practical problems associated with water resources engineering (WRE). GP has a unique feature of introducing explicit models for nonlinear processes in the WRE, which can provide new insight into the understanding of the process. Considering continuous growth of GP and its importance to both water industry and academia, this paper presents a comprehensive review on the recent progress and applications of GP in the WRE fields. Our review commences with brief explanations on the fundamentals of classic GP and its advanced variants (including multigene GP, linear GP, gene expression programming, and grammar-based GP), which have been proven to be useful and frequently used in the WRE. The representative papers having wide range of applications are clustered in three domains of hydrological, hydraulic, and hydroclimatological studies, and outlined or discussed at each domain. Finally, this paper was concluded with discussions of the optimum selection of GP parameters and likely future research directions in the WRE are suggested.

This paper presents an explicit solution for the specific flow depths in partially filled pipes of circular cross-sectional area. Four depths encounter in most classical free-surface flow problems, namely the normal depth, the critical depth, the sequent depths from the specific momentum equation, and the alternate depths from the specific energy equation. This paper proposes new equations derived from dimensional analysis and gene expression programming to estimate directly those flow depths. The equations are examined over a wide range of flow and geometric conditions, providing satisfactory predictions when compared with the exact solution obtained from the governing hydraulic equations. Maximum error encountered in the critical and normal flow depths predictions is less than 1.25%, and maximum error encountered in the alternate and sequent flow depths predictions is less than 3.85%, which are acceptable in most hydraulic engineering practice. The equations are simple and would be useful in hydraulic engineering practice when quick and accurate estimates are needed of those depths, and can also be used to find the initial values for the flow depth in various theoretical and numerical schemes.

This paper presents an explicit solution for the specific flow depths in partially filled pipes of circular cross-sectional area. Four depths encounter in most classical free-surface flow problems, namely the normal depth, the critical depth, the sequent depths from the specific momentum equation, and the alternate depths from the specific energy equation. This paper proposes new equations derived from dimensional analysis and gene expression programming to estimate directly those flow depths. The equations are examined over a wide range of flow and geometric conditions, providing satisfactory predictions when compared with the exact solution obtained from the governing hydraulic equations. Maximum error encountered in the critical and normal flow depths predictions is less than 1.25%, and maximum error encountered in the alternate and sequent flow depths predictions is less than 3.85%, which are acceptable in most hydraulic engineering practice. The equations are simple and would be useful in hydraulic engineering practice when quick and accurate estimates are needed of those depths, and can also be used to find the initial values for the flow depth in various theoretical and numerical schemes.

Accurate prediction of pier scour can lead to economic design of bridge piers and prevent catastrophic incidents. This paper presents the application of self-adaptive evolutionary extreme learning machine (SAELM) to develop a new model for the prediction of local scour around bridge piers using 476 field pier scour measurements with four shapes of piers: sharp, round, cylindrical, and square. The model network parameters are optimized using the differential evolution algorithm. The best SAELM model calculates the scour depth as a function of pier dimensions and the sediment mean diameter. The developed SAELM model had the lowest error indicators when compared to regression-based prediction models for root mean square error (RMSE) (0.15, 0.65, respectively) and mean absolute relative error (MARE) (0.50, 2.0, respectively). The SAELM model was found to perform better than artificial neural networks or support vector machines on the same dataset. Parametric analysis showed that the new model predictions are influenced by pier dimensions and bed-sediment size and produce similar trends of variations of scour-hole depth as reported in literature and previous experimental measurements. The prediction uncertainty of the developed SAELM model is quantified and compared with existing regression-based models and found to be the least, ±0.03 compared with ±0.10 for other models.