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 sel...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Atmospheric Science Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/asl.1276 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832582792827895808 |
|---|---|
| author | Golnar Ghanbarzadeh Khalil Ghorbani Meysam Salarijazi Chooghi Bairam Komaki Laleh Rezaei Ghaleh |
| author_facet | Golnar Ghanbarzadeh Khalil Ghorbani Meysam Salarijazi Chooghi Bairam Komaki Laleh Rezaei Ghaleh |
| author_sort | Golnar Ghanbarzadeh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eed435ad92ed4bd4b410fcc073b197f7 |
| institution | Kabale University |
| issn | 1530-261X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Atmospheric Science Letters |
| spelling | doaj-art-eed435ad92ed4bd4b410fcc073b197f72025-01-29T09:47:21ZengWileyAtmospheric Science Letters1530-261X2025-01-01261n/an/a10.1002/asl.1276Use of stepwise m5 model tree to forecast the P24max based on teleconnection indicesGolnar Ghanbarzadeh0Khalil Ghorbani1Meysam Salarijazi2Chooghi Bairam Komaki3Laleh Rezaei Ghaleh4Department of Water Engineering, Faculty of Water and Soil Engineering Gorgan University of Agricultural Sciences and Natural Resources Gorgan IranDepartment of Water Engineering, Faculty of Water and Soil Engineering Gorgan University of Agricultural Sciences and Natural Resources Gorgan IranDepartment of Water Engineering, Faculty of Water and Soil Engineering Gorgan University of Agricultural Sciences and Natural Resources Gorgan IranDepartment of Arid Regions Management Gorgan University of Agricultural Sciences and Natural Resources Gorgan IranDepartment of Water Engineering, Faculty of Agriculture Urmia University Urmia IranAbstract 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.https://doi.org/10.1002/asl.1276decision treeforecastM5P24maxteleconnection |
| spellingShingle | Golnar Ghanbarzadeh Khalil Ghorbani Meysam Salarijazi Chooghi Bairam Komaki Laleh Rezaei Ghaleh Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices Atmospheric Science Letters decision tree forecast M5 P24max teleconnection |
| title | Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices |
| title_full | Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices |
| title_fullStr | Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices |
| title_full_unstemmed | Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices |
| title_short | Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices |
| title_sort | use of stepwise m5 model tree to forecast the p24max based on teleconnection indices |
| topic | decision tree forecast M5 P24max teleconnection |
| url | https://doi.org/10.1002/asl.1276 |
| work_keys_str_mv | AT golnarghanbarzadeh useofstepwisem5modeltreetoforecastthep24maxbasedonteleconnectionindices AT khalilghorbani useofstepwisem5modeltreetoforecastthep24maxbasedonteleconnectionindices AT meysamsalarijazi useofstepwisem5modeltreetoforecastthep24maxbasedonteleconnectionindices AT chooghibairamkomaki useofstepwisem5modeltreetoforecastthep24maxbasedonteleconnectionindices AT lalehrezaeighaleh useofstepwisem5modeltreetoforecastthep24maxbasedonteleconnectionindices |