Optimization of the Hargreaves, HamonV1, and Penman Potential Evapotranspiration Models Using Bio-inspired Algorithms

Agricultural production is becoming progressively susceptible to water scarcity affecting crop quality and productivity. The implementation of potential evapotranspiration (PET) methods aids in determining the crop water requirements and improving agricultural water management. The focus of this study is to identify the optimum meteorological variables including mean air temperature (T_a), temperature difference(dT), vapor pressure deficit(VPD), wind speed at two meter-height(U₂), net radiation(R_n), and sunshine duration(SD) by employing bio-inspired metaheuristic techniques (genetic algorithm, moth-flame optimization, grey wolf optimization, black widow optimization, and sperm swarm optimization). Three PET methods, Hargreaves, HamonV1, and Penman, were explored to achieve the optimization objective: minimization of PET parameters and estimates. Penman-Monteith model served as a benchmark function to evaluate the other PET models. The optimization resulted in parameter values of 20.72ºC Ta, 1.87ºC dT, 11.25h SD, 59.87 MJ/m2/day Rn, 0.1646 kPa VPD, and 0.28 m/s U2. All swarm intelligence algorithms produced excellent results with near zero(≈0) mean absolute error(MAE), SSO being the most accurate with 2.5e-6 MAE. The hybrid SSO-Penman method provided the PET value closest to that of the PM model. This method and optimized values could serve as motivation for hydrological and agricultural research and applications involving climatic parameters.