Temperature prediction by gene expression programming

Abstract

Air temperature is a crucial climatic component. Because of ever-changing weather, the prediction has evolved into a difficult feat. This research aims to predict the maximum temperature of the central region of Thailand by Gene expression programming (GEP). This technique is a fast and precise prediction technique results using climate measurements from previous years. The variables needed to construct the model are the daily maximum and minimum temperatures, relative humidity, and precipitation. Using Nash-Sutcliffe efficiency (NSE), Root mean square error (RMSE), and coefficient of determination (R2) statistics, the performance of the GEP was examined. The results indicate that the GEP is reliable for predicting daily temperatures.