| 1. |
- Armanuos, Asaad M., et al.
(author)
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Assessing the Effectiveness of Using Recharge Wells for Controlling the Saltwater Intrusion in Unconfined Coastal Aquifers with Sloping Beds : Numerical Study
- 2020
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In: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:7
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Journal article (peer-reviewed)abstract
- Groundwater systems are considered major freshwater sources for many coastal aquifers worldwide. Seawater intrusion (SWI) inland into freshwater coastal aquifers is a common environmental problem that causes deterioration of the groundwater quality. This research investigates the effectiveness of using an injection through a well to mitigate the SWI in sloping beds of unconfined coastal aquifers. The interface was simulated using SEAWAT code. The repulsion ratios due to the length of the SWI wedge (RL) and the area of the saltwater wedge (RA) were computed. A sensitivity analysis was conducted to recognize the change in the confining layer bed slope (horizontal, positive, and negative) and hydraulic parameters of the value of the SWI repulsion ratio. Injection at the toe itself achieved higher repulsion ratios. RL and RA declined if the injection point was located remotely and higher than the toe of the seawater wedge. Installation at the toe achieved a higher RL in positive sloping followed by horizontal and negative slopes. Moreover, the highest value of RA could be reached by injecting at the toe itself with a horizontal bed aquifer, followed by negative and positive slopes. The recharge well is confirmed as one of the most effective applications for the mitigation of SWI in sloping bed aquifers. The Akrotiri case study shows that the proposed recharging water method has a significant impact on controlling SWI and declines in both SWI wedge length and area.
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| 2. |
- Armanuos, Asaad M., et al.
(author)
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Cross Assessment of Twenty-One Different Methods for Missing Precipitation Data Estimation
- 2020
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In: Atmosphere. - Switzerland : MDPI. - 2073-4433. ; 11:4, s. 1-35
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Journal article (peer-reviewed)abstract
- The results of metrological, hydrological, and environmental data analyses are mainlydependent on the reliable estimation of missing data. In this study, 21 classical methods were evaluated to determine the best method for infilling the missing precipitation data in Ethiopia. The monthly data collected from 15 different stations over 34 years from 1980 to 2013 were considered. Homogeneity and trend tests were performed to check the data. The results of the different methods were compared using the mean absolute error (MAE), root-mean-square error (RMSE), coefficient of efficiency (CE), similarity index (S-index), skill score (SS), and Pearson correlation coefficient (rPearson). The results of this paper confirmed that the normal ratio (NR), multiple linear regression (MLR), inverse distance weighting (IDW), correlation coefficient weighting (CCW), and arithmetic average (AA) methods are the most reliable methods of those studied. The NR method provides the most accurate estimations with rPearson of 0.945, mean absolute error of 22.90 mm, RMSE of 33.695 mm, similarity index of 0.999, CE index of 0.998, and skill score of 0.998. When comparing the observed results and the estimated results from the NR, MLR, IDW, CCW, and AA methods, the MAE and RMSE were found to be low, and high values of CE, S-index, SS, and rPearson were achieved. On the other hand, using the closet station (CS), UK traditional, linear regression (LR), expectation maximization (EM), and multiple imputations (MI) methods gave the lowest accuracy, with MAE and RMSE values varying from 30.424 to 47.641 mm and from 49.564 to 58.765 mm, respectively. The results of this study suggest that the recommended methods are applicable for different types of climatic data in Ethiopia and arid regions in other countries around the world.
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| 3. |
- Armanuos, Asaad M., et al.
(author)
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Underground Barrier Wall Evaluation for Controlling Saltwater Intrusion in Sloping Unconfined Coastal Aquifers
- 2020
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In: Water. - Switzerland : MDPI. - 2073-4441. ; 12:9
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Journal article (peer-reviewed)abstract
- Barrier walls are considered one of the most effective methods for facilitating the retreat of saltwater intrusion (SWI). This research plans to examine the effect of using barrier walls for controlling of SWI in sloped unconfined aquifers. The sloping unconfined aquifer is considered with three different bed slopes. The SEAWAT model is implemented to simulate the SWI. For model validation, the numerical results of the seawater wedge at steady state were compared with the analytical solution. Increasing the ratio of flow barrier depth (db/d) forced the saltwater interface to move seaward and increased the repulsion ratio (R). With a positive sloping bed, further embedding the barrier wall from 0.2 to 0.7 caused R to increase from 0.3% to 59%, while it increased from 1.8% to 41.7% and from 3.4% to 46.9% in the case of negative and horizontal slopes, respectively. Embedding the barrier wall to a db/d value of more than 0.4 achieved a greater R value in the three bed-sloping cases. Installing the barrier wall near the saltwater side with greater depth contributed to the retreat of the SWI. With a negative bed slope, moving the barrier wall from Xb/Lo = 1.0 toward the saltwater side (Xb/Lo = 0.2) increased R from 7.21% to 68.75%, whereas R increased from 5.3% to 67% for the horizontal sloping bed and from 5.1% to 64% for the positive sloping bed. The numerical results for the Akrotiri coastal aquifer confirm that the embedment of the barrier wall significantly affects the controlling of SWI by increasing the repulsion ratio (R) and decreasing the SWI length ratio (L/La). Cost-benefit analysis is recommended to determine the optimal design of barrier walls for increasing the cost-effectiveness of the application of barrier walls as a countermeasure for controlling and preventing SWI in sloped unconfined aquifers.
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| 4. |
- Salih, Sinan Q., et al.
(author)
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Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation : case study of Nasser Lake in Egypt
- 2019
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In: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 878-891
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Journal article (peer-reviewed)abstract
- Reliable prediction of evaporative losses from reservoirs is an essential component of reservoir management and operation. Conventional models generally used for evaporation prediction have a number of drawbacks as they are based on several assumptions. A novel approach called the co-active neuro-fuzzy inference system (CANFIS) is proposed in this study for the modeling of evaporation from meteorological variables. CANFIS provides a center-weighted set rather than global weight sets for predictor–predictand relationship mapping and thus it can provide a higher prediction accuracy. In the present study, adjustments are made in the back-propagation algorithm of CANFIS for automatic updating of membership rules and further enhancement of its prediction accuracy. The predictive ability of the CANFIS model is validated with three well-established artificial intelligence (AI) models. Different statistical metrics are computed to investigate the prediction efficacy. The results reveal higher accuracy of the CANFIS model in predicting evaporation compared to the other AI models. CANFIS is found to be capable of modeling evaporation from mean temperature and relative humidity only, with a Nash–Sutcliffe efficiency of 0.93, which is much higher than that of the other models. Furthermore, CANFIS improves the prediction accuracy by 9.2–55.4% compared to the other AI models.
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| 5. |
- Tao, Hai, et al.
(author)
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Global solar radiation prediction over North Dakota using air temperature : Development of novel hybrid intelligence model
- 2021
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In: Energy Reports. - Netherland : Elsevier. - 2352-4847. ; 7, s. 136-157
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Journal article (peer-reviewed)abstract
- Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFISDragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction ccuracy.
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