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Ahmed Sefelnasr
National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates

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Journal article
Published: 24 May 2021 in Sustainability
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In planning and managing water resources, the implementation of optimization techniques in the operation of reservoirs has become an important focus. An optimal reservoir operating policy should take into consideration the uncertainty associated with uncontrolled reservoir inflows. The charged system search (CSS) algorithm model is developed in the present study to achieve optimum operating policy for the current reservoir. The aim of the model is to minimize the cost of system performance, which is the sum of square deviations from the distinction between the release of the target and the actual demand. The decision variable is the release of a reservoir with an initial volume of storage, reservoir inflow, and final volume of storage for a given period. Historical rainfall data is used to approximate the inflow volume. The charged system search (CSS) is developed by utilizing a spreadsheet model to simulate and perform optimization. The model gives the steady-state probabilities of reservoir storage as output. The model is applied to the reservoir of Klang Gates for the development of an optimal reservoir operating policy. The steady-state optimal operating system is used in this model.

ACS Style

Sarmad Latif; Suzlyana Marhain; Shabbir Hossain; Ali Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia. Sustainability 2021, 13, 5900 .

AMA Style

Sarmad Latif, Suzlyana Marhain, Shabbir Hossain, Ali Ahmed, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie. Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia. Sustainability. 2021; 13 (11):5900.

Chicago/Turabian Style

Sarmad Latif; Suzlyana Marhain; Shabbir Hossain; Ali Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. 2021. "Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia." Sustainability 13, no. 11: 5900.

Journal article
Published: 22 March 2021 in Water
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To study the temporal and spatial variations of the groundwater quantity and quality in response to intensive groundwater exploitation from the Quaternary aquifer in UAE, a water budget model with a cell size of one km2 was developed. The available historical records of groundwater levels and salinity have been used to develop the water table and salinity maps of UAE for the years 1969, 2005, 2010, and 2015. The available water resources and soil information system was used to facilitate validity, cogency, and consistency of the groundwater analysis. The spatial analysis module of GIS was used to define the aquifer setting, saturated thickness, aquifer base elevation, effective porosity, and groundwater salinity at each grid cell. The obtained results indicated that the volume of fresh groundwater resources in the Quaternary aquifer in UAE has decreased from 238 km3 in 1969 to around 10 km3 in 2015. A major part of these depleted fresh groundwater resources was replaced by brackish water, and, therefore, the total groundwater storage in this aquifer has only decreased from 977 in 1969 to 922 km3 in 2015, respectively. If the same groundwater exploitation continues, the freshwater storage in the surficial aquifer might be totally depleted in agricultural areas. Most probably, the brackish groundwater resources will be exploited. In such areas, more attention should be devoted to the management of brackish water resources to avoid the exacerbation of the saltwater intrusion problem. Despite the fact that the obtained results indicate the negative impacts of the improper water resources management in a small part of the arid area, the learned lessons are valid for other arid countries, in particular, using the proper steady state boundary conditions for the initial conditions in modeling the available future management alternatives.

ACS Style

Mohsen Sherif; Ahmed Sefelnasr; Abdel Ebraheem; Mohamed Al Mulla; Mohamed Alzaabi; Khaled Alghafli. Spatial and Temporal Changes of Groundwater Storage in the Quaternary Aquifer, UAE. Water 2021, 13, 864 .

AMA Style

Mohsen Sherif, Ahmed Sefelnasr, Abdel Ebraheem, Mohamed Al Mulla, Mohamed Alzaabi, Khaled Alghafli. Spatial and Temporal Changes of Groundwater Storage in the Quaternary Aquifer, UAE. Water. 2021; 13 (6):864.

Chicago/Turabian Style

Mohsen Sherif; Ahmed Sefelnasr; Abdel Ebraheem; Mohamed Al Mulla; Mohamed Alzaabi; Khaled Alghafli. 2021. "Spatial and Temporal Changes of Groundwater Storage in the Quaternary Aquifer, UAE." Water 13, no. 6: 864.

Research article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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To accurately predict tropospheric ozone concentration(O3), it is needed to investigate the variety of artificial intelligence techniques’ performance, such as machine learning, deep learning and hybrid models. This research aims to effectively predict the hourly ozone trend via fewer input variables. This ozone prediction attempt is performed on diversity data of air pollutants (NO2, NOx, CO, SO2) and meteorological parameters (wind-speed and humidity). The historical datasets are collected from 3 sites in Malaysia. The study’s methodology progressed in two paths: standalone and hybrid models where hourly-averaged datasets are applied based on 5-time horizon analysis scenario, with different inputs’ combinations. For evaluation, all models are tested throughout 5-performance indicator and illustrated on Modified Taylor diagram. Sensitivity analysis of input variables is quantified. Additionally, uncertainty analysis is conducted to assess their confidence level associated with Willmott Index. Based on R2, results indicated that XGBoost has higher accuracy compared to MLP and SVR; meanwhile, LSTM and CNN outweighs XGBoost. In terms of robustness and accuracy, the proposed hybrid model possesses superlative performance compared to all above-mentioned techniques. The proposed model achieved exceptional results as the highest R2, the highest 95% confidence degree, and narrower confidence interval width, are 93.48%, 98.16%, and 0.0014195, respectively.

ACS Style

Ayman Yafouz; Ali Najah Ahmed; Nur’Atiah Zaini; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms. Engineering Applications of Computational Fluid Mechanics 2021, 15, 902 -933.

AMA Style

Ayman Yafouz, Ali Najah Ahmed, Nur’Atiah Zaini, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie. Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):902-933.

Chicago/Turabian Style

Ayman Yafouz; Ali Najah Ahmed; Nur’Atiah Zaini; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. 2021. "Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 902-933.

Research article
Published: 07 November 2020 in Alexandria Engineering Journal
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Developing water losses and reservoir final storage forecast has become an increasingly important task for reservoir operation. Accurate forecasts would lead to better monitoring of water quality and more efficient reservoir operation. Therefore, the flash flood and water crisis problems in Malaysia can be reduced. Artificial neural networks (ANN) models with radial basis function (RBF) have been determined for high efficiency and accuracy, especially in the dynamics system. In this study, the proposed ANN Prediction Model is being developed by using inflow, the release of dam, initial and final storage of the reservoir as input, whereas the water losses from the reservoir as output. All the data collected over 11 years (1997–2007) at Klang Gate reservoir has been used to develop and test model output. The results indicated that the proposed model could provide monthly forecasting with maximum root mean square error of ± 20.07%. The advantages of this ANN model are to provide information for water losses, final storage, and variation of water level for better reservoir operation.

ACS Style

Sarmad Dashti Latif; Ali Najah Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. Reservoir water balance simulation model utilizing machine learning algorithm. Alexandria Engineering Journal 2020, 60, 1365 -1378.

AMA Style

Sarmad Dashti Latif, Ali Najah Ahmed, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie. Reservoir water balance simulation model utilizing machine learning algorithm. Alexandria Engineering Journal. 2020; 60 (1):1365-1378.

Chicago/Turabian Style

Sarmad Dashti Latif; Ali Najah Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. 2020. "Reservoir water balance simulation model utilizing machine learning algorithm." Alexandria Engineering Journal 60, no. 1: 1365-1378.

Journal article
Published: 13 August 2020 in Journal of Water Process Engineering
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The effectiveness of bank filtration (BF) is highly dependent on the source water quality (e.g., organic matter composition, pH, and concentration of heavy metals (HMs)). In this study, the impact of dissolved organic matter (DOM) on the removal of selected metals (Cu, Zn, Pb, Se, and Ni) during BF was investigated. Column studies were conducted at 30 °C with feed water sources of different organic matter composition. Excitation–emission matrix fluorescence coupled with parallel factor analysis (PARAFAC–EEM) was used to characterise the organic composition of the feed waters. Moreover, another series of column studies was conducted to assess the impact of natural organic matter type (humic, protein) and concentration on the HMs removals. The experimental results revealed a high Pb(II) removal efficiency during filtration, which depends only slightly on the organic matter content of the feed water. In comparison, the removals of Cu, Zn and Ni ranged between 65 and 95 %; and relied significantly on the organic concentration and composition in the raw waters. Humic compounds (terrestrial or microbial) demonstrated adequate ability to reduce the removal efficiencies of these HMs during the infiltration. Conversely, biodegradable matter was found to be effective in enhancing the sorption of HMs onto the sand grains. The Se-removal was enhanced when the feed water contained a higher concentration of biodegradable matter. In general, it can be concluded that the organic composition of the source water affects profoundly the removal of HMs during the BF, and should be considered in the design of BF systems.

ACS Style

A. Abdelrady; J. Bachwenkizi; S. Sharma; Ahmed Sefelnasr; M. Kennedy. The fate of heavy metals during bank filtration: Effect of dissolved organic matter. Journal of Water Process Engineering 2020, 38, 101563 .

AMA Style

A. Abdelrady, J. Bachwenkizi, S. Sharma, Ahmed Sefelnasr, M. Kennedy. The fate of heavy metals during bank filtration: Effect of dissolved organic matter. Journal of Water Process Engineering. 2020; 38 ():101563.

Chicago/Turabian Style

A. Abdelrady; J. Bachwenkizi; S. Sharma; Ahmed Sefelnasr; M. Kennedy. 2020. "The fate of heavy metals during bank filtration: Effect of dissolved organic matter." Journal of Water Process Engineering 38, no. : 101563.

Journal article
Published: 24 June 2020 in Water
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Bank filtration (BF) is acknowledged as a sustainable and effective technique to provide drinking water of adequate quality; it has been known for a long time in Europe. However, this technique is site-specific and therefore its application in developing countries with different hydrologic and environment conditions remains limited. In this research, a 3-discipline study was performed to evaluate the feasibility of the application of this technique in Aswan City (Egypt). Firstly, a hydrological model was developed to identify key environmental factors that influence the effectiveness of BF, and to formulate plans for the design and management of the BF system. Secondly, water samples were collected for one year (January 2017 to December 2017) from the water sources and monitoring wells to characterize the bank-filtrate quality. Lastly, an economic study was conducted to compare the capital and operating costs of BF and the existing treatment techniques. The results demonstrated that there is high potential for application of BF under such hydrological and environmental conditions. However, there are some aspects that could restrict the BF efficacy and must therefore be considered during the design process. These include the following: (i) Over-pumping practices can reduce travel time, and thus decrease the efficiency of treatment; (ii) Locating the wells near the surface water systems (<50 m) decreases the travel time to the limit (<10 days), and thus could restrict the treatment capacity. In such case, a low pumping rate must be applied; (iii) the consequences of lowering the surface water level can be regulated through the continuous operation of the wells. Furthermore, laboratory analysis indicated that BF is capable of producing high quality drinking water. However, an increase in organic matter (i.e., humics) concentration was observed in the pumped water, which increases the risk of trihalomethanes being produced if post-chlorination is implemented. The economic study ultimately demonstrated that BF is an economic and sustainable technique for implementation in Aswan City to address the demand for potable water.

ACS Style

Ahmed Abdelrady; Saroj Sharma; Ahmed Sefelnasr; Mustafa El-Rawy; Maria Kennedy. Analysis of the Performance of Bank Filtration for Water Supply in Arid Climates: Case Study in Egypt. Water 2020, 12, 1816 .

AMA Style

Ahmed Abdelrady, Saroj Sharma, Ahmed Sefelnasr, Mustafa El-Rawy, Maria Kennedy. Analysis of the Performance of Bank Filtration for Water Supply in Arid Climates: Case Study in Egypt. Water. 2020; 12 (6):1816.

Chicago/Turabian Style

Ahmed Abdelrady; Saroj Sharma; Ahmed Sefelnasr; Mustafa El-Rawy; Maria Kennedy. 2020. "Analysis of the Performance of Bank Filtration for Water Supply in Arid Climates: Case Study in Egypt." Water 12, no. 6: 1816.

Review
Published: 26 May 2020 in Sustainability
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The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water treatment plants depend on the concentration of nitrogen in streams. Considering the stochastic nature and the various hydrological variables upon which nitrogen concentration depends, a predictive model should be efficient enough to account for all the complexities of nature in the prediction of nitrogen concentration. For two decades, artificial neural networks (ANNs) and other models (such as autoregressive integrated moving average (ARIMA) model, hybrid model, etc.), used for predicting different complex hydrological parameters, have proved efficient and accurate up to a certain extent. In this review paper, such prediction models, created for predicting nitrogen concentration, are critically analyzed, comparing their accuracy and input variables. Moreover, future research works aiming to predict nitrogen using advanced techniques and more reliable and appropriate input variables are also discussed.

ACS Style

Pavitra Kumar; Sai Hin Lai; Jee Khai Wong; Nuruol Syuhadaa Mohd; Rowshon Kamal; Haitham Abdulmohsin Afan; Ali Najah Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models. Sustainability 2020, 12, 4359 .

AMA Style

Pavitra Kumar, Sai Hin Lai, Jee Khai Wong, Nuruol Syuhadaa Mohd, Rowshon Kamal, Haitham Abdulmohsin Afan, Ali Najah Ahmed, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie. Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models. Sustainability. 2020; 12 (11):4359.

Chicago/Turabian Style

Pavitra Kumar; Sai Hin Lai; Jee Khai Wong; Nuruol Syuhadaa Mohd; Rowshon Kamal; Haitham Abdulmohsin Afan; Ali Najah Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models." Sustainability 12, no. 11: 4359.

Journal article
Published: 14 May 2020 in Entropy
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In this study, the analysis of the extreme sea level was carried out by using 10 years (2007–2016) of hourly tide gauge data of Karachi port station along the Pakistan coast. Observations revealed that the magnitudes of the tides usually exceeded the storm surges at this station. The main observation for this duration and the subsequent analysis showed that in June 2007 a tropical Cyclone “Yemyin” hit the Pakistan coast. The joint probability method (JPM) and the annual maximum method (AMM) were used for statistical analysis to find out the return periods of different extreme sea levels. According to the achieved results, the AMM and JPM methods erre compatible with each other for the Karachi coast and remained well within the range of 95% confidence. For the JPM method, the highest astronomical tide (HAT) of the Karachi coast was considered as the threshold and the sea levels above it were considered extreme sea levels. The 10 annual observed sea level maxima, in the recent past, showed an increasing trend for extreme sea levels. In the study period, the increment rates of 3.6 mm/year and 2.1 mm/year were observed for mean sea level and extreme sea level, respectively, along the Karachi coast. Tidal analysis, for the Karachi tide gauge data, showed less dependency of the extreme sea levels on the non-tidal residuals. By applying the Merrifield criteria of mean annual maximum water level ratio, it was found that the Karachi coast was tidally dominated and the non-tidal residual contribution was just 10%. The examination of the highest water level event (13 June 2014) during the study period, further favored the tidal dominance as compared to the non-tidal component along the Karachi coast.

ACS Style

Faisal Ahmed Khan; Tariq Masood Ali Khan; Ali Najah Ahmed; Haitham Abdulmohsin Afan; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. Complex Extreme Sea Levels Prediction Analysis: Karachi Coast Case Study. Entropy 2020, 22, 549 .

AMA Style

Faisal Ahmed Khan, Tariq Masood Ali Khan, Ali Najah Ahmed, Haitham Abdulmohsin Afan, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie. Complex Extreme Sea Levels Prediction Analysis: Karachi Coast Case Study. Entropy. 2020; 22 (5):549.

Chicago/Turabian Style

Faisal Ahmed Khan; Tariq Masood Ali Khan; Ali Najah Ahmed; Haitham Abdulmohsin Afan; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. 2020. "Complex Extreme Sea Levels Prediction Analysis: Karachi Coast Case Study." Entropy 22, no. 5: 549.

Journal article
Published: 13 March 2020 in Scientific Reports
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In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.

ACS Style

Haitham Abdulmohsin Afan; Mohammed Falah Allawi; Amr El-Shafie; Zaher Mundher Yaseen; Ali Najah Ahmed; Marlinda Abdul Malek; Suhana Binti Koting; Sinan Q. Salih; Wan Hanna Melini Wan Mohtar; Sai Hin Lai; Ahmed Sefelnasr; Mohsen Sherif; Ahmed El-Shafie. Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting. Scientific Reports 2020, 10, 4684 -15.

AMA Style

Haitham Abdulmohsin Afan, Mohammed Falah Allawi, Amr El-Shafie, Zaher Mundher Yaseen, Ali Najah Ahmed, Marlinda Abdul Malek, Suhana Binti Koting, Sinan Q. Salih, Wan Hanna Melini Wan Mohtar, Sai Hin Lai, Ahmed Sefelnasr, Mohsen Sherif, Ahmed El-Shafie. Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting. Scientific Reports. 2020; 10 (1):4684-15.

Chicago/Turabian Style

Haitham Abdulmohsin Afan; Mohammed Falah Allawi; Amr El-Shafie; Zaher Mundher Yaseen; Ali Najah Ahmed; Marlinda Abdul Malek; Suhana Binti Koting; Sinan Q. Salih; Wan Hanna Melini Wan Mohtar; Sai Hin Lai; Ahmed Sefelnasr; Mohsen Sherif; Ahmed El-Shafie. 2020. "Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting." Scientific Reports 10, no. 1: 4684-15.

Journal article
Published: 07 February 2020 in Sustainability
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This study aims to investigate the impact of meteorological parameters such as wind direction, wind speed, rainfall, and mean cloud cover on sea-level rise projections for different time horizons—2019, 2023, 2028, 2048, and 2068—at three stations located in Kudat, Sandakan, and Kota Kinabalu, which are districts in the state of Sabah, Malaysia. Herein, two different scenarios, scenario1 (SC1) and scenario2 (SC2), were investigated, with each scenario comprising a different combination of input parameters. This study proposes two artificial intelligence techniques: a multilayer perceptron neural network (MLP-ANN) and an adaptive neuro-fuzzy inference system (ANFIS). Furthermore, three evaluation indexes were adopted to assess the performance of the proposed models. These indexes are the correlation coefficient, root mean square error, and scatter index. The trial and error method were used to tune the hyperparameters: the number of neurons in the hidden layer, training algorithms, transfer and activation functions, and number and shape of the membership function for the proposed models. Results show that for the above mentioned three stations, the ANFIS model outperformed MLP-ANN by 0.740%, 6.23%, and 9.39%, respectively. To assess the uncertainties of the best model, ANFIS, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPUs) and the band width of 95 percent confidence intervals (d-factors) are selected. The obtained values bracketed by 95PPUs are show about 75.2%, 77.4%, 76.8% and the d-factor has a value of 0.27, 0.21 and 0.23 at Kudat, Sandakan and Kota Kinabalu stations, respectively. A comparison between the two scenarios shows that SC1 achieved a high level of accuracy on Kudat and Sandakan data, whereas SC2 outperformed SC1 on Kota Kinabalu data.

ACS Style

T. Olivia Muslim; Ali Najah Ahmed; M. A. Malek; Haitham Abdulmohsin Afan; Rusul Khaleel Ibrahim; Amr El-Shafie; Michelle Sapitang; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. Investigating the Influence of Meteorological Parameters on the Accuracy of Sea-Level Prediction Models in Sabah, Malaysia. Sustainability 2020, 12, 1193 .

AMA Style

T. Olivia Muslim, Ali Najah Ahmed, M. A. Malek, Haitham Abdulmohsin Afan, Rusul Khaleel Ibrahim, Amr El-Shafie, Michelle Sapitang, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie. Investigating the Influence of Meteorological Parameters on the Accuracy of Sea-Level Prediction Models in Sabah, Malaysia. Sustainability. 2020; 12 (3):1193.

Chicago/Turabian Style

T. Olivia Muslim; Ali Najah Ahmed; M. A. Malek; Haitham Abdulmohsin Afan; Rusul Khaleel Ibrahim; Amr El-Shafie; Michelle Sapitang; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. 2020. "Investigating the Influence of Meteorological Parameters on the Accuracy of Sea-Level Prediction Models in Sabah, Malaysia." Sustainability 12, no. 3: 1193.

Journal article
Published: 01 February 2020 in Journal of African Earth Sciences
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ACS Style

Ahmed Abdelhalim; Ahmed Sefelnasr; Esam Ismail. Response of the interaction between surface water and groundwater to climate change and proposed megastructure. Journal of African Earth Sciences 2020, 162, 1 .

AMA Style

Ahmed Abdelhalim, Ahmed Sefelnasr, Esam Ismail. Response of the interaction between surface water and groundwater to climate change and proposed megastructure. Journal of African Earth Sciences. 2020; 162 ():1.

Chicago/Turabian Style

Ahmed Abdelhalim; Ahmed Sefelnasr; Esam Ismail. 2020. "Response of the interaction between surface water and groundwater to climate change and proposed megastructure." Journal of African Earth Sciences 162, no. : 1.

Journal article
Published: 07 January 2020 in Journal of Environmental Management
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Bank filtration (BF) has been used for many years as an economical technique for providing high-quality drinking water. However, under anaerobic conditions, the aquifer release of undesirable metal(loid)s, such as iron manganese, and arsenic, reduces the bank filtrate quality and thus restricts the application of this technique. This study investigates the impact of the organic-matter composition of source water on the mobilisation of Fe, Mn, and As during the anaerobic BF process. A laboratory-scale column study was conducted at a controlled-temperature (30 ± 2 °C) using different feed water sources. The organic matter characteristics of the feed water were elucidated using excitation-emission spectroscopy techniques integrated with parallel factor framework clustering analysis (PFFCA) model. The experiment was performed at redox conditions between 66 mv and −185 mv. Moreover, batch studies were implemented to study the effect of natural organic matter type (humic, fulvic and tyrosine) and concentration on the mobilisation of the selected metal(loids). The laboratory experiments demonstrated that the mobilisation of Fe, Mn and As during the BF are varied with the organic water concentration and composition of the source water. The fluorescence results revealed that terrestrial and condensed structure humic compounds are more capable to release Fe into the filtrate water. In contrast, Mn exhibited an equal tendency of mobilisation towards all the humic compounds regardless of its origin and structure. However, at a humic concentration higher than 5 mg-C/L, Mn showed more affinity towards lower molecular weight humic compounds. Arsenic was found to be the least impacted by the alteration in the source water organic matter composition; its mobilisation was highly correlated with iron releasing process. On the other hand, the biodegradable organic matter at high concentration (>10 mg-C/L) was found to be highly effective to turn the infiltration area into Fe-reducing environment and thereby elevating Fe and As concentrations in the pumped water. In conclusion, this study revealed that the DOM composition and concentration of the raw water could play an important role in the mobilisation of metal(loids) during the BF processes.

ACS Style

Ahmed Abdelrady; Saroj Sharma; Ahmed Sefelnasr; Maria Kennedy. Characterisation of the impact of dissolved organic matter on iron, manganese, and arsenic mobilisation during bank filtration. Journal of Environmental Management 2020, 258, 110003 .

AMA Style

Ahmed Abdelrady, Saroj Sharma, Ahmed Sefelnasr, Maria Kennedy. Characterisation of the impact of dissolved organic matter on iron, manganese, and arsenic mobilisation during bank filtration. Journal of Environmental Management. 2020; 258 ():110003.

Chicago/Turabian Style

Ahmed Abdelrady; Saroj Sharma; Ahmed Sefelnasr; Maria Kennedy. 2020. "Characterisation of the impact of dissolved organic matter on iron, manganese, and arsenic mobilisation during bank filtration." Journal of Environmental Management 258, no. : 110003.

Articles
Published: 01 January 2020 in Engineering Applications of Computational Fluid Mechanics
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High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.

ACS Style

Ellysia Jumin; Nuratiah Zaini; Ali Najah Ahmed; Samsuri Abdullah; Marzuki Ismail; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction. Engineering Applications of Computational Fluid Mechanics 2020, 14, 713 -725.

AMA Style

Ellysia Jumin, Nuratiah Zaini, Ali Najah Ahmed, Samsuri Abdullah, Marzuki Ismail, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie. Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction. Engineering Applications of Computational Fluid Mechanics. 2020; 14 (1):713-725.

Chicago/Turabian Style

Ellysia Jumin; Nuratiah Zaini; Ali Najah Ahmed; Samsuri Abdullah; Marzuki Ismail; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. 2020. "Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction." Engineering Applications of Computational Fluid Mechanics 14, no. 1: 713-725.

Journal article
Published: 27 November 2019 in Arabian Journal of Geosciences
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Groundwater utilization in agriculture and domestic uses has increased so there is emerging mindfulness for groundwater monitoring pollution. The expense of groundwater observation especially in large areas is costly so geographic information system (GIS)-based DRASTIC model become an absolute necessity to discover the groundwater vulnerable zones to preserve groundwater quality by means of monitoring the vulnerable areas. In this work, generic and pesticide DRASTIC based on geographic information system (GIS) were accomplished. Regarding the generic DRASTIC model, the detected vulnerability classes covering the area are moderate, high, and very high, which represent 28.4%, 58.9%, and 12.7%, respectively. However, in the vulnerability map generated by pesticide DRASTIC model, the results indicated that about 22.4% of the study area has a very high vulnerability to contamination, 61.4% has high vulnerability, and 16.2% has moderate vulnerability.

ACS Style

Zenhom E. Salem; Ahmed M. Sefelnasr; Samia S. Hasan. Assessment of groundwater vulnerability for pollution using DRASTIC Index, young alluvial plain, Western Nile Delta, Egypt. Arabian Journal of Geosciences 2019, 12, 1 -13.

AMA Style

Zenhom E. Salem, Ahmed M. Sefelnasr, Samia S. Hasan. Assessment of groundwater vulnerability for pollution using DRASTIC Index, young alluvial plain, Western Nile Delta, Egypt. Arabian Journal of Geosciences. 2019; 12 (23):1-13.

Chicago/Turabian Style

Zenhom E. Salem; Ahmed M. Sefelnasr; Samia S. Hasan. 2019. "Assessment of groundwater vulnerability for pollution using DRASTIC Index, young alluvial plain, Western Nile Delta, Egypt." Arabian Journal of Geosciences 12, no. 23: 1-13.

Journal article
Published: 26 November 2019 in Sustainability
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Drought, climate change, and demand make precipitation forecast a very important issue in water resource management. The present study aims to develop a forecasting model for monthly precipitation in the basin of the province of East Azarbaijan in Iran over a ten-year period using the multilayer perceptron neural network (MLP) and support vector regression (SVR) models. In this study, the flow regime optimization algorithm (FRA) was applied to optimize the multilayer neural network and support vector machine. The flow regime optimization algorithm not only identifies the parameters of the SVR and MLP models but also replaces the training algorithms. The decision tree model (M5T) was also used to forecast precipitation and compare it with the results of hybrid models. Principal component analysis (PCA) was used to identify effective indicators for precipitation forecast. In the first scenario, the input data include temperature data with a delay of one to twelve months, the second scenario includes precipitation data with a delay of one to twelve months, and the third scenario includes precipitation and temperature data with a delay of one to three months. The mean absolute error (MAE) and Nash–Sutcliffe error (NSE) indices were used to evaluate the performance of the models. The results showed that the proposed MLP–FRA outperformed all the other examined models. Regarding the uncertainties of the models, it was also shown that the MLP–FRA model had a lower uncertainty band width than other models, and a higher percentage of the data will fall within the range of the confidence band. As the selected scenario, Scenario 3 had a better performance. Finally, monthly precipitation maps were generated based on the MLP–FRA model and Scenario 3 using the weighted interpolation method, which showed significant precipitation in spring and winter and a low level of precipitation in summer. The results of the present study showed that MLP–FRA has high capability to predict hydrological variables and can be used in future research.

ACS Style

Fatemeh Barzegari Banadkooki; Mohammad Ehteram; Ali Najah Ahmed; Chow Ming Fai; Haitham Abdulmohsin Afan; Wani M. Ridwam; Ahmed Sefelnasr; Ahmed El-Shafie. Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models. Sustainability 2019, 11, 6681 .

AMA Style

Fatemeh Barzegari Banadkooki, Mohammad Ehteram, Ali Najah Ahmed, Chow Ming Fai, Haitham Abdulmohsin Afan, Wani M. Ridwam, Ahmed Sefelnasr, Ahmed El-Shafie. Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models. Sustainability. 2019; 11 (23):6681.

Chicago/Turabian Style

Fatemeh Barzegari Banadkooki; Mohammad Ehteram; Ali Najah Ahmed; Chow Ming Fai; Haitham Abdulmohsin Afan; Wani M. Ridwam; Ahmed Sefelnasr; Ahmed El-Shafie. 2019. "Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models." Sustainability 11, no. 23: 6681.

Journal article
Published: 14 October 2019 in Arabian Journal of Geosciences
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The Quaternary aquifer in Assiut Governorate is the most important water resource necessary for irrigation and development in Assiut Governorate. The study area is located between latitudes 26° 47′–27° 37′ N and longitudes 30° 37′–31° 34′ E, covering about 2500 km2. This study is an integrated GIS-supported approach proposed to create and develop a transient three-dimensional groundwater flow model for the Quaternary aquifer in Assiut Governorate. Based on the prevailing climatic, environmental, developmental, and water demand conditions, the model was designed to investigate the most feasible groundwater management option. In this context, a great focus was given to the impact of the construction of the Assiut new barrages on the groundwater situation in the study area. According to the actual and supposed extraction rates of the Quaternary aquifer, six scenarios were suggested; however, only four of them are presented here. For the construction of rigid potentiometric isolines, the available records of water levels in more than 540 wells were used. The model was calibrated under the steady state and transient conditions using the trial and error method. The period 2007–2010 was chosen as a calibration period based on the availability and temporal distribution of the data. The simulation of the actual extraction rates (scenario 1, 767 × 106 m3/y) indicated that by 2050, the biggest drawdown is happening at the northwestern part with an average value of 8 m. In contrast, the groundwater is rising by about 6 m by 2050 in the same scenario. The simulation of the full capacity, proposed extraction rates (scenario 3, 1534 × 106 m3/y) resulted in severe changes of the hydraulic head patterns within almost all of the study area of the Quaternary aquifer during the simulation period. This scenario showed clearly a 12-m drawdown which occurs at the northeast part of the study area. Scenario 4 supposed a decrease in the water level of the River Nile by 1 m, a general increase in the groundwater level was detected by the end of the simulation. In scenario 5, the new location of the Assiut new barrage was simulated; the upstream water level has been considered the same as the old one and, however, is displaced 500 m downstream. By 2025, the impact of this scenario was observed by the increase in the hydraulic head in Assiut city by about 0.1 to 0.2 m. During the calibration process, it has been learned that there should be a groundwater recharge from the western and eastern plateau; however, this conclusion still needs further studies to confirm it.

ACS Style

Ahmed Mostafa Sefelnasr; Awad Abdel-Khalek Omran; Hassan Ali Abdel-Hak; Waleed Sayed El Tahawy. GIS-based numerical modeling for the groundwater assessment: a case study in the Quaternary aquifer, Assiut Governorate, Egypt. Arabian Journal of Geosciences 2019, 12, 1 -13.

AMA Style

Ahmed Mostafa Sefelnasr, Awad Abdel-Khalek Omran, Hassan Ali Abdel-Hak, Waleed Sayed El Tahawy. GIS-based numerical modeling for the groundwater assessment: a case study in the Quaternary aquifer, Assiut Governorate, Egypt. Arabian Journal of Geosciences. 2019; 12 (20):1-13.

Chicago/Turabian Style

Ahmed Mostafa Sefelnasr; Awad Abdel-Khalek Omran; Hassan Ali Abdel-Hak; Waleed Sayed El Tahawy. 2019. "GIS-based numerical modeling for the groundwater assessment: a case study in the Quaternary aquifer, Assiut Governorate, Egypt." Arabian Journal of Geosciences 12, no. 20: 1-13.

Journal article
Published: 12 February 2019 in Water
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Estimations of natural radioactivity levels were carried out for water (surface and groundwater) samples collected from the west bank of the Nile River in Assiut Governorate, Egypt. The activity concentrations in the water samples ranged from 19.20 ± 2.40 to 492.26 ± 71.52 mBq/L, from 15.58 ± 2.62 to 351.39 ± 66.13 mBq/L, and from 50.31 ± 5.58 to 2255.03 ± 249.42 mBq/L for 226Ra, 232Th, and 40K, respectively. In this work, the recorded activity concentrations have been organized statistically using a dendrogram cluster and a principal coordinate analysis. In view of the groupings of radionuclide activity, the average annual effective doses through ingestion for adults, children, and infants, despite the responsibility of each explicit radionuclide to the total dose, were assessed and debated. Children had the most important measurement calculations, making them the most regarded mass gathering. All estimations for each different water type, as well as for each individual population group, scored well under the recommended reference value of 0.1 mSv resulting from a one year’s intake of drinking water in accordance with the recommendations of the European Commission (EC) in 1998.

ACS Style

Hany El-Gamal; Ahmed Sefelnasr; Ghada Salaheldin. Determination of Natural Radionuclides for Water Resources on the West Bank of the Nile River, Assiut Governorate, Egypt. Water 2019, 11, 311 .

AMA Style

Hany El-Gamal, Ahmed Sefelnasr, Ghada Salaheldin. Determination of Natural Radionuclides for Water Resources on the West Bank of the Nile River, Assiut Governorate, Egypt. Water. 2019; 11 (2):311.

Chicago/Turabian Style

Hany El-Gamal; Ahmed Sefelnasr; Ghada Salaheldin. 2019. "Determination of Natural Radionuclides for Water Resources on the West Bank of the Nile River, Assiut Governorate, Egypt." Water 11, no. 2: 311.

Journal article
Published: 11 February 2019 in Arabian Journal of Geosciences
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The demand for freshwater supplies is progressively ascending owing to the increase of the population expansion and economic growth. Available water resources have been reduced by pollution and over-pumping. Groundwater modeling is a powerful tool for water resources management, groundwater protection, and remediation. The aim of this study is to develop a numerical groundwater flow model for the Quaternary aquifer in Samalut city, Minia Governorate, Egypt. The model is used to determine the hydrogeological conditions of the aquifer, the flow directions as well as calculating the rates of recharge and discharge between surface water and groundwater in the study area. Furthermore, scenarios were designed in the model to assess the response of the aquifer to increase the groundwater extraction in the future. The model was calibrated by trial and error; simulated results were compared to the observed head and contour maps, which were generally in good agreement. No typical steady-state condition is prevailed in the aquifer and groundwater flow directions are toward northeast direction. The River Nile acts as a drain in the study area, while El-Ibrahimiya Canal and Bahr Yusef act as a source of aquifer recharge. The proposed scenarios showed that surface water plays an important role in recharging the aquifer during increasing groundwater extraction. The results showed that the change in the aquifer storage will be decreased from + 48,125 m3/day in the current state (2013) to + 27,134 m3/day and − 869 m3/day when the groundwater extraction is increased by 25% and 50%, respectively.

ACS Style

Ahmed Abdelhalim; Ahmed Sefelnasr; Esam Ismail. Numerical modeling technique for groundwater management in Samalut city, Minia Governorate, Egypt. Arabian Journal of Geosciences 2019, 12, 1 -18.

AMA Style

Ahmed Abdelhalim, Ahmed Sefelnasr, Esam Ismail. Numerical modeling technique for groundwater management in Samalut city, Minia Governorate, Egypt. Arabian Journal of Geosciences. 2019; 12 (4):1-18.

Chicago/Turabian Style

Ahmed Abdelhalim; Ahmed Sefelnasr; Esam Ismail. 2019. "Numerical modeling technique for groundwater management in Samalut city, Minia Governorate, Egypt." Arabian Journal of Geosciences 12, no. 4: 1-18.

Journal article
Published: 14 January 2019 in Journal of Environmental Chemical Engineering
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Riverbank filtration (RBF) represents a low-cost and sustainable alternative to advanced treatment technologies to pre-treat or remove several organic micropollutants (OMPs) from surface water. The objective of this research was to investigate the efficacy of biodegradation and adsorption processes in the removal of OMPs at high temperatures (20-30 ± 2 °C) during RBF. Laboratory-scale batch studies were conducted using silica sand at different temperatures (20, 25 and 30 °C) to study the removal of 19 OMPs (6 polyaromatic hydrocarbons (PAHs), 8 herbicides and 5 insecticides) from various water sources with different organic matter characteristics. Simazine, atrazine, metolachlor, and isoproturon exhibited partial persistent characters (16% < removal < 59%), which apparently decreased with increase in temperature. DDT, pyriproxyfen, pendimethalin, β-BHC, endosulfan sulfate and PAHs with high hydrophobicity (solubility in terms of logS < -4) tend to be well adsorbed onto sand grains (removal > 80%), regardless of temperature, redox conditions or type of organic carbon fraction fed to the batch reactors. These findings indicate that these hydrophobic compounds are effectively removed during RBF regardless of the environmental conditions. Hydrophilic compounds (molinate, dimethoate, and propanil) showed temperature-dependent characteristics for influent water with low organic matter; their attenuation increased at higher temperature (removal > 95%) due to the high microbial activity. This study revealed that temperature is an important parameter affecting the removal of OMPs with hydrophilic and low-hydrophobicity characters. However, temperature has less influence on the removal of highly hydrophobic OMPs during RBF process and thus should be considered during RBF system design.

ACS Style

Ahmed Abdelrady; Saroj Sharma; Ahmed Sefelnasr; Amr Abogbal; Maria Kennedy. Investigating the impact of temperature and organic matter on the removal of selected organic micropollutants during bank filtration: A batch study. Journal of Environmental Chemical Engineering 2019, 7, 102904 .

AMA Style

Ahmed Abdelrady, Saroj Sharma, Ahmed Sefelnasr, Amr Abogbal, Maria Kennedy. Investigating the impact of temperature and organic matter on the removal of selected organic micropollutants during bank filtration: A batch study. Journal of Environmental Chemical Engineering. 2019; 7 (1):102904.

Chicago/Turabian Style

Ahmed Abdelrady; Saroj Sharma; Ahmed Sefelnasr; Amr Abogbal; Maria Kennedy. 2019. "Investigating the impact of temperature and organic matter on the removal of selected organic micropollutants during bank filtration: A batch study." Journal of Environmental Chemical Engineering 7, no. 1: 102904.

Journal article
Published: 26 November 2018 in Water
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Dissolved organic matter (DOM) in source water highly influences the removal of different contaminants and the dissolution of aquifer materials during bank filtration (BF). The fate of DOM during BF processes under arid climate conditions was analysed by conducting laboratory—scale batch and column studies under different environmental conditions with varying temperature (20–30 °C), redox, and feed water organic matter composition. The behaviour of the DOM fractions was monitored using various analytical techniques: fluorescence excitation-emission matrix spectroscopy coupled with parallel factor analysis (PARAFAC-EEM), and size exclusion liquid chromatography with organic carbon detection (LC-OCD). The results revealed that DOM attenuation is highly dependent (p < 0.05) on redox conditions and temperature, with higher removal at lower temperatures and oxic conditions. Biopolymers were the fraction most amenable to removal by biodegradation (>80%) in oxic environments irrespective of temperature and feed water organic composition. This removal was 20–24% lower under sub-oxic conditions. In contrast, the removal of humic compounds exhibited a higher dependency on temperature. PARAFAC-EEM revealed that terrestrial humic components are the most temperature critical fractions during the BF processes as their sorption characteristics are negatively correlated with temperature. In general, it can be concluded that BF is capable of removing labile compounds under oxic conditions at all water temperatures; however, its efficiency is lower for humic compounds at higher temperatures.

ACS Style

Ahmed AbdelRady; Saroj Sharma; Ahmed Sefelnasr; Maria Kennedy. The Fate of Dissolved Organic Matter (DOM) During Bank Filtration under Different Environmental Conditions: Batch and Column Studies. Water 2018, 10, 1730 .

AMA Style

Ahmed AbdelRady, Saroj Sharma, Ahmed Sefelnasr, Maria Kennedy. The Fate of Dissolved Organic Matter (DOM) During Bank Filtration under Different Environmental Conditions: Batch and Column Studies. Water. 2018; 10 (12):1730.

Chicago/Turabian Style

Ahmed AbdelRady; Saroj Sharma; Ahmed Sefelnasr; Maria Kennedy. 2018. "The Fate of Dissolved Organic Matter (DOM) During Bank Filtration under Different Environmental Conditions: Batch and Column Studies." Water 10, no. 12: 1730.