Estimation of daily dew point temperature by using bat algorithm optimization based extreme learning machine

Abstract Capabilities of the bat algorithm optimized extreme learning machine (Bat-ELM) model for dew point temperature (Tdew) estimation were evaluated in this study, in comparison with the kernel-based nonlinear extension of Arps decline model (KNEA), the genetic algorithm optimized ELM (GA-ELM), the particle swarm optimization ELM (PSO-ELM), and six other non-hybrid machine learning models. Daily meteorological data [including mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), mean relative humidity (RHmean), maximum relative humidity (RHmax), minimum relative humidity (RHmin) and atmospheric pressure (Pa)] during 2014–2017 at the Yangling station of China were collected for model evaluation, by using six different input combinations and a 10-fold cross-validation. Results showed that all models exhibited a poor accuracy with Tmean as the only input, but had a relatively good accuracy under the combination of three meteorological parameters (i.e., Tmax, Tmin and Pa) that can be easily acquired. Under the combination of Tmax, Tmin, RHmax, RHmin and Pa, model performances were similar or even slightly worse when compared with the combination of Tmax, Tmin, RHmax and RHmin. Overall, for estimating daily Tdew, our results suggest that Bat-ELM would be the optimal model while Tmax, Tmin, RHmax and RHmin would be the best input combinations.