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Your search keyword '"Sammen, Saad Shauket"' showing total 6 results
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5. Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test.

Author

Malik, Anurag, Tikhamarine, Yazid, Al-Ansari, Nadhir, Shahid, Shamsuddin, Sekhon, Harkanwaljot Singh, Pal, Raj Kumar, Rai, Priya, Pandey, Kusum, Singh, Padam, Elbeltagi, Ahmed, and Sammen, Saad Shauket

Subjects
PARTICLE swarm optimization, AGRICULTURAL resources, ALGORITHMS, WATER management, MATHEMATICAL optimization
Abstract

Ensuring accurate estimation of evaporation is weighty for effective planning and judicious management of available water resources for agricultural practices. Thus, this work enhances the potential of support vector regression (SVR) optimized with a novel nature-inspired algorithm, namely, Slap Swarm Algorithm (SVR-SSA) against Whale Optimization Algorithm (SVR-WOA), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Particle Swarm Optimization (SVR-PSO), and Penman model (PM). Daily EP (pan-evaporation) was estimated in two different agro-climatic zones (ACZ) in northern India. The optimal combination of input parameters was extracted by applying the Gamma test (GT). The outcomes of the hybrid of SVR and PM models were equated with recorded daily EP observations based on goodness-of-fit measures along with graphical scrutiny. The results of the appraisal showed that the novel hybrid SVR-SSA-5 model performed superior (MAE = 0.697, 1.556, 0.858 mm/day; RMSE = 1.116, 2.114, 1.202 mm/day; IOS = 0.250, 0.350, 0.303; NSE = 0.0.861, 0.750, 0.834; PCC = 0.929, 0.868, 0.918; IOA = 0.960, 0.925, 0.956) than other models in testing phase at Hisar, Bathinda, and Ludhiana stations, respectively. In conclusion, the hybrid SVR-SSA model was identified as more suitable, robust, and reliable than the other models for daily EP estimation in two different ACZ. [ABSTRACT FROM AUTHOR]

Published
2021
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6. Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models.

Author

Sihag, Parveen, Dursun, Omer Faruk, Sammen, Saad Shauket, Malik, Anurag, and Chauhan, Anita

Subjects
SOFT computing, FLUMES, REGRESSION analysis, STANDARD deviations, PEARSON correlation (Statistics), NONLINEAR regression
Abstract

In this study, the potential of soft computing techniques, namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH), was evaluated to predict the aeration efficiency (AE20) of Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models i.e., with multiple linear regression (MLR) and multiple nonlinear regression (MNLR). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting AE20 of Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) ? 0.9997, and 0.9992, and root mean square error (RMSE)? 0.0015, and 0.0045 during calibration and validation periods, respectively. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE20 of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE20 of Parshall and Modified Venturi flumes. [ABSTRACT FROM AUTHOR]

Published
2021
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