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

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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 ( IF 6.1 ) Pub Date : 2021-07-02 , DOI: 10.1080/19942060.2021.1942990
Anurag Malik ₁ , Yazid Tikhamarine _{2,

3} , Nadhir Al-Ansari ₄ , Shamsuddin Shahid ₅ , Harkanwaljot Singh Sekhon ₆ , Raj Kumar Pal ₁ , Priya Rai ₇ , Kusum Pandey ₈ , Padam Singh ₉ , Ahmed Elbeltagi ₁₀ , Saad Shauket Sammen ₁₁

Affiliation

Punjab Agricultural University, Regional Research Station, Bathinda, Punjab, India
Southern Public Works Laboratory (LTPS), Tamanrasset, Algeria
Department of Science and Technology, University Centre of Tamanrasset, Tamanrasset, Algeria
Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden
School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia
Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana, India
Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana, India
College of Forestry, Veer Chandra Singh Garhwali Uttarakhand University of Horticulture and Forestry, Ranichauri, Tehri Garhwal, Uttarakhand, India
Agricultural Engineering Dept., Faculty of Agriculture, Mansoura University, Mansoura, Egypt
Department of Civil Engineering, College of Engineering, Diyala University, Diyala 15 Governorate, Iraq

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.

中文翻译：

使用由 Salp swarm 算法优化的新型混合支持向量回归结合伽马测试，估计不同农业气候区的每日蒸发量

确保准确估算蒸发量对于农业实践中可用水资源的有效规划和明智管理非常重要。因此，这项工作增强了支持向量回归 (SVR) 的潜力，该算法使用一种新颖的自然启发算法进行优化，即针对鲸鱼优化算法 (SVR-WOA) 的 Slap Swarm 算法 (SVR-SSA)、Multi-Verse Optimizer (SVR- MVO)、斑点鬣狗优化器 (SVR-SHO)、粒子群优化 (SVR-PSO) 和 Penman 模型 (PM)。在印度北部的两个不同的农业气候带 (ACZ) 中估计了每日 EP（泛蒸发）。通过应用 Gamma 测试 (GT) 提取输入参数的最佳组合。SVR 和 PM 模型混合的结果等同于基于拟合优度测量和图形审查记录的每日 EP 观察结果。评估结果表明，新型混合SVR-SSA-5模型表现优异（MAE = 0.697、1.556、0.858 mm/天；RMSE = 1.116、2.114、1.202 mm/天；IOS = 0.250、0.350、NSE 0.303） = 0.0.861, 0.750, 0.834; PCC = 0.929, 0.868, 0.918; IOA = 0.960, 0.925, 0.956) 在 Hisar, Bathinda 和 Ludhiana 站的测试阶段分别比其他模型。总之，在两个不同的 ACZ 中，混合 SVR-SSA 模型被认为比其他模型更适合、稳健和可靠，用于每日 EP 估计。和卢迪亚纳站，分别。总之，在两个不同的 ACZ 中，混合 SVR-SSA 模型被认为比其他模型更适合、稳健和可靠，用于每日 EP 估计。和卢迪亚纳站，分别。总之，在两个不同的 ACZ 中，混合 SVR-SSA 模型被认为比其他模型更适合、稳健和可靠，用于每日 EP 估计。

更新日期：2021-07-04

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