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 available water resources agricultural practices. Thus, this work enhances the potential support vector regression (SVR) optimized with a novel nature-inspired algorithm, namely, Slap Swarm Algorithm (SVR-SSA) against Whale Optimization (SVR-WOA), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena (SVR-SHO), Particle (SVR-PSO), Penman model (PM). Daily EP (pan-evaporation) was estimated in two different agro-climatic zones (ACZ) northern India. The optimal combination input parameters extracted by applying Gamma test (GT). outcomes hybrid SVR PM models were equated recorded daily observations based on goodness-of-fit measures along graphical scrutiny. results appraisal showed that SVR-SSA-5 performed superior (MAE = 0.697, 1.556, 0.858 mm/day; RMSE 1.116, 2.114, 1.202 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 testing phase at Hisar, Bathinda, Ludhiana stations, respectively. In conclusion, SVR-SSA identified as more suitable, robust, reliable ACZ.