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
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 Swa...
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my.utm.976952022-10-31T01:08:40Z http://eprints.utm.my/id/eprint/97695/ 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 Malik, Anurag Tikhamarine, Yazid Al-Ansari, Nadhir Shahid, Shamsuddin Sekhon, Harkanwaljot Singh Pal, Raj Kumar Rai, Priya Pandey, Kusum Singh, Padam Ahmed Elbeltagi, Ahmed Elbeltagi Sammen, Saad Shauket TA Engineering (General). Civil engineering (General) 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. Taylor and Francis Ltd. 2021 Article PeerReviewed Malik, Anurag and Tikhamarine, Yazid and Al-Ansari, Nadhir and Shahid, Shamsuddin and Sekhon, Harkanwaljot Singh and Pal, Raj Kumar and Rai, Priya and Pandey, Kusum and Singh, Padam and Ahmed Elbeltagi, Ahmed Elbeltagi and Sammen, Saad Shauket (2021) 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. Engineering Applications of Computational Fluid Mechanics, 15 (1). pp. 1075-1094. ISSN 1994-2060 http://dx.doi.org/10.1080/19942060.2021.1942990 DOI : 10.1080/19942060.2021.1942990 |
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TA Engineering (General). Civil engineering (General) Malik, Anurag Tikhamarine, Yazid Al-Ansari, Nadhir Shahid, Shamsuddin Sekhon, Harkanwaljot Singh Pal, Raj Kumar Rai, Priya Pandey, Kusum Singh, Padam Ahmed Elbeltagi, Ahmed Elbeltagi Sammen, Saad Shauket 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 |
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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. |
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Malik, Anurag Tikhamarine, Yazid Al-Ansari, Nadhir Shahid, Shamsuddin Sekhon, Harkanwaljot Singh Pal, Raj Kumar Rai, Priya Pandey, Kusum Singh, Padam Ahmed Elbeltagi, Ahmed Elbeltagi Sammen, Saad Shauket |
author_facet |
Malik, Anurag Tikhamarine, Yazid Al-Ansari, Nadhir Shahid, Shamsuddin Sekhon, Harkanwaljot Singh Pal, Raj Kumar Rai, Priya Pandey, Kusum Singh, Padam Ahmed Elbeltagi, Ahmed Elbeltagi Sammen, Saad Shauket |
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Malik, Anurag |
title |
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 |
title_short |
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 |
title_full |
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 |
title_fullStr |
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 |
title_full_unstemmed |
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 |
title_sort |
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 |
publisher |
Taylor and Francis Ltd. |
publishDate |
2021 |
url |
http://eprints.utm.my/id/eprint/97695/ http://dx.doi.org/10.1080/19942060.2021.1942990 |
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1748180495335686144 |
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13.18916 |