| 1. |
- Ebeed, Mohamed, et al.
(författare)
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Solving stochastic optimal reactive power dispatch using an Adaptive Beluga Whale optimization considering uncertainties of renewable energy resources and the load growth
- 2024
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Ingår i: Ain Shams Engineering Journal. - : ELSEVIER. - 2090-4479 .- 2090-4495. ; 15:7
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Tidskriftsartikel (refereegranskat)abstract
- The electrical system performance can be improved considerably by controlling the reactive power flow in the system. The reactive power control can be achieved by optimal reactive power dispatch (ORPD) problem solution and optimal integration of the FACTS devices. With high penetration of renewable energy sources (RESs) and the load growth, the ORPD solution became a challenging and a complex task due to the stochastic nature of the RERs and the load growth. In this regard, the aim of this paper is to solve the stochastic optimal reactive power dispatch (SORPD) with optimal inclusion of PV units, wind turbines and the unified power flow controller (UPFC) under uncertainties of the load growth and the generated powers. An Adaptive Beluga Whale Optimization (ABWO) is proposed for solving the SORPD which is based on the Fitness-Distance Balance Selection (FDBS) strategy and the territorial solitary males' strategy of the Mountain Gazelle Optimizer. The proposed ABWO is tested on IEEE 30-bus system and a comparison with other optimization techniques for solving the ordinary ORPD is presented for validating the proposed ABWO. The obtained results reveal that the TEPL is reduced from 5.3168 MW to 3.97985 MW with optimal integration of the RERs and UPFC. Likewise, the TEVD is reduced from 0.1794p.u. to 0.10689p.u. and the TVSI is decreased from 0.1289p.u. to 0.0476p.u.
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| 2. |
- Ehteram, Mohammad, et al.
(författare)
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Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms
- 2021
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Ingår i: Ain Shams Engineering Journal. - : Ain Shams University. - 2090-4479 .- 2090-4495. ; 12:2, s. 1665-1676
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Tidskriftsartikel (refereegranskat)abstract
- The infiltration process during irrigation is an essential variable for better water management and hence there is a need to develop an accurate model to estimate the amount infiltration water during irrigation. However, the fact that the infiltration process is a highly non-linear procedure and hence required special modeling approach to accurately mimic the infiltration procedure. Therefore, the ability of Adaptive Neuro-Fuzzy Interface System (ANFIS) models in estimating infiltrated water during irrigation in the furrow for sustainable management is proposed. The main innovation of current research is the first attempt to employ the ANFIS model for predicating infiltration rates, in addition, integrate the ANFIS model with three new optimization algorithms. Three optimizing algorithms, viz. Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) were used to tune the ANFIS-parameters. Experimental data from six different studies in different countries have been used in this study to validate the proposed model. The inflow rate, furrow length, infiltration opportunity time, cross-sectional area, and waterfront advance time have been utilized as the input parameters. The results indicated that the ANFIS-SCA could provide a better estimation for the infiltration rate compared to ANFIS-PSO. The Mean Absolute Error (MAE) and Percent Bias (PBIAS) errors computed for the ANIFS-SCA (0.007 m3/m and 0.12) was significantly better than those achieved from the ANFIS-FFA and the ANFIS-PSO In addition to that, ANIFS-SCA model outperformed ANFIS-FFA with high level of accuracy. The proposed Hybrid ANFIS-SCA showed outstanding performance over the other optimizer algorithms in estimating the infiltration rate and could be applied in different irrigation systems for better sustainable irrigation management.
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| 3. |
- Ghalla, Mohamed, et al.
(författare)
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Novel sustainable techniques for enhancing shear strength of RC beams mitigating construction failure risk
- 2024
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Ingår i: Ain Shams Engineering Journal. - 2090-4479 .- 2090-4495.
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study is to evaluate the effectiveness of various innovative and sustainable methods for improving the shear performance of reinforced concrete (RC) beams. The potential risk of failure for such elements is considered a potential threat, therefore, this study addresses it through experimental tests and numerical analyses to be mitigated carefully in order to enhance the safety and sustainability of buildings. A total of eleven specimens, comprising two control specimens and nine strengthened specimens, underwent three-point testing. Several proposed strengthening techniques, each involving multiple parameters, were examined. In the initial approach, glass fiber-reinforced polymer (GFRP) textile embedded in an external fiber-reinforced cementitious mortar (FRCM) jacket was utilized, with an evaluation of the number of the GFRP textile layers (1, 2, and 3 layers). The second technique incorporated near surface mounted (NSM) GFRP bars along with the FRCM jacketing, where the diameter of the GFRP bars (10, 12, and 16 mm) served as the primary parameter. In the final technique, externally bonded stainless-steel strips (SSSs) of varying thicknesses (1, 1.25, 1.50 mm) were affixed to the beams’ surface. The obtained results revealed that the application of the FRCM jacketing method yielded positive results, showing a significant 30.7 % average increase in the crack initiation load and a 17.1 % improvement in the failure load compared to the defected beam. However, issues of debonding beneath the loading point were observed in the FRCM jacket, particularly with three layers of the GFRP textile, leading to the separation of the concrete cover. Moreover, combining the NSM GFRP bars with an FRCM jacket addressed the absence of shear stirrups. The most remarkable improvement was noted utilizing the NSM GFRP bars and an FRCM jacket, followed by employing SSSs with an FRCM jacket.
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| 4. |
- Hammad Khaliq, Ahmad, et al.
(författare)
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Spatiotemporal landslide susceptibility mapping using machine learning models: A case study from district Hattian Bala, NW Himalaya, Pakistan
- 2023
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Ingår i: Ain Shams Engineering Journal. - : Elsevier. - 2090-4479 .- 2090-4495. ; 14:3
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Tidskriftsartikel (refereegranskat)abstract
- The Himalayan region, a rugged mountain zone is among the most susceptible zones to the landslide hazard due to its terrain, geography, and active tectonics. Machine learning (ML) techniques are most advanced and precise methods to develop landslide susceptibility model (LSM). The current study was designed to analyze and assess the landslide susceptibility using ML approaches for District Hattian Bala, NW Himalayas, Pakistan. The historical satellite imageries are used to generate spatiotemporal landslide inventories of year 2005, 2007 and 2012. A spatial database was created pertaining to topographic, environmental, geologic, and anthropogenic factors including slope, aspect, elevation, curvature, plane curvature, profile curvature, topographic wetness index (TWI), lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI) and land use/ land cover (LULC). These LCFs were selected to analyze periodic landslide susceptibility in the region. The experimental design utilized 349, 393, and 735 landslide inventory of 2005, 2007, and 2012 respectively. Two ML models, i.e., Random Forest (RF) and Logistic Regression (LR) were applied to assess landslide susceptibility determine by thirteen landslide causative factors (LCFs). The spatiotemporal landslide inventory was partitioned into training (70%) and testing (30%) landslides for respective years to check the prediction accuracies of selected ML models. Comparative analysis of different LSMs was performed by the Receiver Operator Curves – Area Under Curves (ROC-AUC). The resultant accuracy, MAE, RMSE, Kappa, Precision, Recall, F1 indicated that RF outperformed the LR model. The study aims to minimize losses to lives and potential economic damage linked with recurrent slope instabilities in the region. It is anticipated that use of ML algorithms would support concerned authorities and organizations to effectively plan and manage landslide hazard in the region.
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| 5. |
- Latif, Sarmad Dashti, et al.
(författare)
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Development of prediction model for phosphate in reservoir water system based machine learning algorithms
- 2022
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Ingår i: Ain Shams Engineering Journal. - : Elsevier. - 2090-4479 .- 2090-4495. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Phosphate (PO4) is a major component of most fertilizers, and when erosion and runoff occur, large amounts of it enter the water bodies, causing several problems such as eutrophication. Feitsui reservoir, the primary source of water supply to Taipei, reported half of the reservoir's pollutants from nonpoint-source pollution. The value of the PO4 in the water body fluctuates in highly nonlinear and stochastic patterns. However, conventional modeling techniques are no longer sufficiently effective in predicting accurately such stochastic patterns in the concentrations of PO4 in water. Therefore, this study proposes different machine learning algorithms: the artificial neural network (ANN), support vector machine (SVM), random forest (RF), and boosted trees (BT) to predict the concentration of PO4. Monthly measured data between 1986 and 2014 were used to train and test the accuracy of these models. The performances of these models were examined using different statistical indices. Hyperparameters optimization such as cross-validation was performed to enhance the precision of the models. Five water quality parameters were used as input to the proposed models. Different input combinations were explored to optimize the precision. The findings revealed that ANN outperformed the other three models to capture the changes in the concentrations of PO4 with high precision where RMSE is equal to 1.199, MAE is equal to 0.858, and R2 is equal to 0.979, MSE is equal to 1.439, and finally, CC is equal to 0.9909. The developed model could be used as a reliable means for managing eutrophication problems.
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| 6. |
- Mohammadi, Babak, et al.
(författare)
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Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation
- 2022
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Ingår i: Ain Shams Engineering Journal. - : Ain Shams University. - 2090-4479 .- 2090-4495. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Solar radiation plays a pivotal role in the energy balance at the Earth's surface, evaporation, snow melting, water requirements of plants, and hydrological control of catchments. In this work, performance of ERA-Interim (a reanalysis dataset) was examined to estimate solar radiation at Ahvaz, BandarAbbas, and Kermanshah weather stations representing the even spatial distribution over Iran using eight empirical models and an artificial intelligence-based model (SVM: Support Vector Machine). In the calibration set, SVM exhibited the best performance with RMSEs of 249, 299 and 437 J.cm−2.day−1 at the aforementioned stations, respectively. In validation set, SVM reduced the errors in the estimates of solar radiation by 2.5 and 7.3 percent compared to the best empirical model at Ahvaz station (Abdallah model, RMSE = 242 J.cm−2.day−1) and Kermanshah station (Angstrom-Prescott model, RMSE = 315 J.cm−2.day−1), respectively. During the validation at BandarAbbas station, Bahel and Abdallah model (RMSE = 309 J.cm−2.day−1), Angstrom-Prescott model (RMSE = 310 J.cm−2.day−1) and SVM (RMSE = 312 J.cm−2.day−1) showed a relatively similar performance. The results also showed that the ERA-Interim dataset can be a comparatively suitable alternative to some of the empirical models, where radiation or the input parameters of empirical models are not directly measured, with RMSEs of 382.81, 320.82 and 414.1 J.cm−2.day−1 at Ahvaz, BandarAbbas, and Kermanshah stations, respectively (in validation phase); although its error rates are significant compared with the SVM model, and substituting it for artificial intelligence-based models is not recommended.
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| 7. |
- Qais, Mohammed H., et al.
(författare)
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Early outlier detection in three-phase induction heating systems using clustering algorithms
- 2024
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Ingår i: Ain Shams Engineering Journal. - : Elsevier. - 2090-4479 .- 2090-4495. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Induction heating (IH) devices transfer the electric power to the contactless cookware via the electromagnetic field. Therefore, the temperature of cookware is measured remotely, and the early detection of cookware overheating will ensure the user’s safety as well as extend the remaining useful life of electronic components. Therefore, this work presents a clustering model for outlier detection in IH systems based on clustering algorithms and measured data using two thermal sensors. First, a healthy dataset is collected for the temperatures of inverters and cookware under different sizes and materials of cookware items, different amounts of water in cookware, and different amounts of electrical power. After that, K-means and fuzzy c-means were utilized to cluster this normal dataset, where the maximum distance between their centers and data points was selected as a threshold. Finally, the clustered model is investigated using a testing dataset that includes outliers. According to the results, the K-means algorithm detected around 96% of the produced outliers, however, the fuzzy c-means algorithm detected around 68%. In conclusion, the deployment of the clustering model in outlier detection is simple and uses only the threshold and the cluster centers.
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| 8. |
- Sharafati, Ahmad, et al.
(författare)
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Application of bagging ensemble model for predicting compressive strength of hollow concrete masonry prism
- 2021
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Ingår i: Ain Shams Engineering Journal. - : Elsevier. - 2090-4479 .- 2090-4495. ; 12:4, s. 3521-3530
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Tidskriftsartikel (refereegranskat)abstract
- In the current research, a newly developed ensemble intelligent predictive model called Bagging Regression (BGR) is proposed to predict the compressive strength of a hollow concrete masonry prism (fp). A matrix of input combinations is constructed based on several predictive variables, including mortar compressive strength (fm), concrete block compressive strength (fb), and height to thickness ratio (h/t). Three modeling scenarios based on the different data divisions (i.e., 80–20%, 75–25%, and 70–30%) for training-testing phases are evaluated. The proposed model is validated against classical support vector regression (SVR) and decision tree regression (DTR) models using statistical indicators and graphical presentations. Results indicate the superiority of the BGR over the other models. In quantitative terms, BGR attains minimum root mean square error (RMSE = 1.51 MPa) using the data division scenario of 80–20% in the testing phase, while DTR and standalone SVR models offer RMSE = 2.55 and 2.33 MPa, respectively.
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| 9. |
- Achite, Mohammed, et al.
(författare)
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Application of multiple spatial interpolation approaches to annual rainfall data in the Wadi Cheliff basin (north Algeria)
- 2024
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Ingår i: Ain Shams Engineering Journal. - 2090-4479. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- This study addresses a challenging problem of predicting mean annual precipitation across arid and semi-arid areas in northern Algeria, utilizing deterministic, geostatistical (GS), and machine learning (ML) models. Through the analysis of data spanning nearly five decades and encompassing 150 monitoring stations, the result of Random Forest showed the highest training performance, with R square value (of 0.9524) and the Root Mean Square Error (of 24.98). Elevation emerges as a critical factor, enhancing prediction accuracy in mountainous and complex terrains when used as an auxiliary variable. Cluster analysis further refines our understanding of station distribution and precipitation characteristics, identifying four distinct clusters, each exhibiting unique precipitation patterns and elevation zones. This study helps for a better understanding of precipitation prediction, encouraging the integration of additional variables and the exploration of climate change impacts, thereby contributing to informed environmental management and adaptation strategies across diverse climatic and terrain scenarios.
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| 10. |
- Emamgholizadeh, Samad, et al.
(författare)
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Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea
- 2023
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Ingår i: Ain Shams Engineering Journal. - : Elsevier BV. - 2090-4479. ; 14:2
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Tidskriftsartikel (refereegranskat)abstract
- Cation exchange capacity (CEC) has a key role in soil studies such as agriculture, energy balance, characteristics of the soil for food, maintaining water in the soil as well as soil pollution management. Its measurement is difficult and time-consuming. So, its prediction using artificial intelligent (AI) models with soil readily available properties can be the proper solution. In this study, the physical and chemical properties of the soil, such as pH, EC, organic carbon, clay content, sands, and total nitrogen used as input data for the AI models. The adaptive-network-based fuzzy inference system (ANFIS), ANFIS model coupled by differential evolution (ANFIS-DE), and ANFIS model coupled by particle swarm optimization (ANFIS-PSO) are used for the prediction of the CEC. Then the ability of those methods in the prediction of the CEC. Results showed higher efficiency of the coupled models (ANFIS-DE and ANFIS-PSO) compared to the ordinary ANFIS model.
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