Prediction vs. estimation of dewpoint temperature: assessing GEP, MARS and RF models

Abstract

AbstractDewpoint temperature (Tdew) plays a key role in agricultural issues as well as meteorological studies. This paper is aimed at developing and validating prediction and estimation models of Tdew values. Gene expression programming (GEP), multivariate adaptive regression spline (MARS), and random forest (RF) models were employed. Data from six weather stations (consisting of a period of ten years) in East Azerbaijan, northwestern Iran were utilized for establishing, testing, and validating the models. In the case of predicting models, chronological records of Tdew in previous time steps were introduced as models' inputs to predict Tdew values at daily and weekly prediction intervals. In the case of Tdew estimating models, daily records of mean air temperature, sunshine hours, relative humidity, and wind speed were utilized as inputs to estimate Tdew. Acquired results showed prediction-based GEP surpasses the MARS and RF models in both daily and weekly prediction intervals. Among the estimation models, the MARS models that relied on air temperature, relative humidity, and sunshine hours presented the most accurate results in all studied locations as well as the studied region. The current study proposes the use of MARS models in estimating Tdew magnitudes, while it criticizes the use of single data set assignment for both temporal and spatial analysis.