Predictability of relative humidity by two artificial intelligence techniques using noisy data from two Californian gauging stations

Abstract

Recorded time series of relative humidity (RH) are modeled by using genetic expression programming (GEP) and artificial neural networks (ANNs) models. The data are noisy and contain missing datapoints. RH is modeled as a function of three meteorological variables: temperature, wind speed, and pressure. Various model structures of both of these models are investigated with the aim of testing the robustness of the predicted values in the presence of noise and missing data. Due to the presence of noise, a sophisticated treatment of missing data was not justifiable, and therefore, the strategy adopted was just to carry the datapoints backward, although this may induce bias in the time dimension and contaminate the predicted results. The results of this study indicate that through a careful selection of model structures both GEP and ANN can produce adequately reliable prediction of RH values 1 year into the future. The paper provides evidence that this model structure is feasible when the dependent variables include both the present and past values.