Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting
Abstract Precipitation due to its complex nature requires a comprehensive model for forecasting purposes and the efficiency of improved ARIMA (IARIMA) forecasts has been proved relative to the conventional models. This study used two procedures in the structure of IARIMA to obtain accurate monthly p...
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Nature Portfolio
2024-05-01
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| Online Access: | https://doi.org/10.1038/s41598-024-63046-3 |
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| author | Laleh Parviz Mansour Ghorbanpour |
| author_facet | Laleh Parviz Mansour Ghorbanpour |
| author_sort | Laleh Parviz |
| collection | DOAJ |
| description | Abstract Precipitation due to its complex nature requires a comprehensive model for forecasting purposes and the efficiency of improved ARIMA (IARIMA) forecasts has been proved relative to the conventional models. This study used two procedures in the structure of IARIMA to obtain accurate monthly precipitation forecasts in four stations located in northern Iran; Bandar Anzali, Rasht, Ramsar, and Babolsar. The first procedure applied support vector regression (SVR) for modeling the statistical characteristics and monthly precipitation of each class, IARIMA-SVR, which improved the evaluation metrics so that the decrease of Theil's coefficient and average relative variance in all stations was 21.14% and 17.06%, respectively. Two approaches are defined in the second procedure which includes a forecast combination (C) scheme, IARIMA-C-particle swarm optimization (PSO), and artificial intelligence technique. Generally, most of the time, IARIMA-C-PSO relative to the other approach, exhibited acceptable results and the accuracy improvement was greater than zero at all stations. Comparing the two procedures, it is found that the capability of IARIMA-C-PSO is higher concerning the IARIMA-SVR, so the decrease in the normalized mean squared error value from IARIMA to IARIMA-SVR and IARIMA-C-PSO is 36.72% and 39.92%, respectively for all stations. The residual predictive deviation (RPD) of IARIMA-C-PSO for all stations is greater than 2, which indicates the high performance of the model. With a comprehensive investigation, the performance of Bandar Anzali station is better than the other stations. By developing an improved ARIMA model, one can achieve a high performance in structure identifying and forecasting of monthly time series which is one of the issues of interest and importance. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2024-05-01 |
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| spelling | doaj-art-a1882820782045c5aa7b81c6e7f193f22025-01-12T12:25:16ZengNature PortfolioScientific Reports2045-23222024-05-0114111710.1038/s41598-024-63046-3Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecastingLaleh Parviz0Mansour Ghorbanpour1Faculty of Agriculture, Azarbaijan Shahid Madani UniversityDepartment of Medicinal Plants, Faculty of Agriculture and Natural Resources, Arak UniversityAbstract Precipitation due to its complex nature requires a comprehensive model for forecasting purposes and the efficiency of improved ARIMA (IARIMA) forecasts has been proved relative to the conventional models. This study used two procedures in the structure of IARIMA to obtain accurate monthly precipitation forecasts in four stations located in northern Iran; Bandar Anzali, Rasht, Ramsar, and Babolsar. The first procedure applied support vector regression (SVR) for modeling the statistical characteristics and monthly precipitation of each class, IARIMA-SVR, which improved the evaluation metrics so that the decrease of Theil's coefficient and average relative variance in all stations was 21.14% and 17.06%, respectively. Two approaches are defined in the second procedure which includes a forecast combination (C) scheme, IARIMA-C-particle swarm optimization (PSO), and artificial intelligence technique. Generally, most of the time, IARIMA-C-PSO relative to the other approach, exhibited acceptable results and the accuracy improvement was greater than zero at all stations. Comparing the two procedures, it is found that the capability of IARIMA-C-PSO is higher concerning the IARIMA-SVR, so the decrease in the normalized mean squared error value from IARIMA to IARIMA-SVR and IARIMA-C-PSO is 36.72% and 39.92%, respectively for all stations. The residual predictive deviation (RPD) of IARIMA-C-PSO for all stations is greater than 2, which indicates the high performance of the model. With a comprehensive investigation, the performance of Bandar Anzali station is better than the other stations. By developing an improved ARIMA model, one can achieve a high performance in structure identifying and forecasting of monthly time series which is one of the issues of interest and importance.https://doi.org/10.1038/s41598-024-63046-3Improved ARIMAMonthlySVRIARIMA-C-PSORPD |
| spellingShingle | Laleh Parviz Mansour Ghorbanpour Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting Scientific Reports Improved ARIMA Monthly SVR IARIMA-C-PSO RPD |
| title | Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting |
| title_full | Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting |
| title_fullStr | Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting |
| title_full_unstemmed | Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting |
| title_short | Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting |
| title_sort | assimilation of pso and svr into an improved arima model for monthly precipitation forecasting |
| topic | Improved ARIMA Monthly SVR IARIMA-C-PSO RPD |
| url | https://doi.org/10.1038/s41598-024-63046-3 |
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