Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices

Abstract

In this study, the linear and non‐linear multivariate relationships between 25 teleconnection indices (tele‐indices) as independent variables and annual P24max as the dependent variable were analyzed using multivariate linear regression (MLR) and decision tree regression models (M5), in selected synoptic weather stations of Iran over a statistical period of 30 years (1992–2021). No strong and statistically significant correlation between each tele‐index and P24max was observed. Therefore, it is not appropriate to attribute climate changes in the region to a single factor such as El Niño, but rather consider the combined influence of multiple factors. The M5 model demonstrated higher performance, indicating a non‐linear relationship between tele‐indices and P24max. The stepwise execution of the M5 model tree showed that the algorithm follows a greedy approach, and it is not necessary to use all variables to predict P24max. The normalized root mean square error (NRMSE) of P24max estimation was found to be 15%, 13%, 15%, 8%, 20%, 14%, and 12% with the coefficients of determination of 0.78, 0.79, 0.72, 0.85, 0.81, 0.82, and 0.84 in Hashemabad‐Gorgan, Rasht, Kermanshah, Ahvaz, Bandar Abbas, Isfahan, and Birjand, respectively. Finally, it is possible to forecast P24max using tele‐indices measured in the previous year.