Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition
Study region: Six regions in Algeria have been selected as follows: Ain Elhadjel, Msaad, Boussaada, Elkantara, M'sila and M'doukel. Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variational Mode Decomposition (VMD) technique and t...
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Elsevier B.V.
2025
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| author | Ladouali S. Katipo?lu O.M. Bahrami M. Kartal V. Sakaa B. Elshaboury N. Keblouti M. Chaffai H. Ali S. Pande C.B. Elbeltagi A. |
| author2 | 59169490100 |
| author_facet | 59169490100 Ladouali S. Katipo?lu O.M. Bahrami M. Kartal V. Sakaa B. Elshaboury N. Keblouti M. Chaffai H. Ali S. Pande C.B. Elbeltagi A. |
| author_sort | Ladouali S. |
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| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Study region: Six regions in Algeria have been selected as follows: Ain Elhadjel, Msaad, Boussaada, Elkantara, M'sila and M'doukel. Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variational Mode Decomposition (VMD) technique and the Extreme Learning Machine (ELM) algorithm as a preprocessing technique for predicting future droughts. The first 6 and 12-month SPI values 1, 2, and 3-month lead time values were estimated with the ELM algorithm. After that, meteorological variables and Standard Precipitation Index (SPI) values, divided into subcomponents with VMD, are presented to the ELM model, and a drought forecasting model is developed. Model performances were evaluated according to various visual and statistical criteria. New hydrological insights for the region: Soft computing techniques have become the preferred method for producing predictions due to their ability to minimize development time, require minimal input, and offer a relatively less complex approach when compared to dynamic or physical models. As a result of the analysis, it has been determined that the highest prediction accuracies are generally obtained in VMD-ELM models and SPI predictions with a 1-month lead time. The study outputs give important ideas to mite donors regarding water resource planning and climate change adaptation strategies in the study area and can be applied to other arid and semi-arid environments. ? 2024 The Authors |
| format | Article |
| id | my.uniten.dspace-36468 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | my.uniten.dspace-364682025-03-03T15:42:34Z Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition Ladouali S. Katipo?lu O.M. Bahrami M. Kartal V. Sakaa B. Elshaboury N. Keblouti M. Chaffai H. Ali S. Pande C.B. Elbeltagi A. 59169490100 57203751801 57194685752 57221197958 55346347400 57216611553 55951389400 55345356800 57208073787 57193547008 57204724397 Study region: Six regions in Algeria have been selected as follows: Ain Elhadjel, Msaad, Boussaada, Elkantara, M'sila and M'doukel. Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variational Mode Decomposition (VMD) technique and the Extreme Learning Machine (ELM) algorithm as a preprocessing technique for predicting future droughts. The first 6 and 12-month SPI values 1, 2, and 3-month lead time values were estimated with the ELM algorithm. After that, meteorological variables and Standard Precipitation Index (SPI) values, divided into subcomponents with VMD, are presented to the ELM model, and a drought forecasting model is developed. Model performances were evaluated according to various visual and statistical criteria. New hydrological insights for the region: Soft computing techniques have become the preferred method for producing predictions due to their ability to minimize development time, require minimal input, and offer a relatively less complex approach when compared to dynamic or physical models. As a result of the analysis, it has been determined that the highest prediction accuracies are generally obtained in VMD-ELM models and SPI predictions with a 1-month lead time. The study outputs give important ideas to mite donors regarding water resource planning and climate change adaptation strategies in the study area and can be applied to other arid and semi-arid environments. ? 2024 The Authors Final 2025-03-03T07:42:34Z 2025-03-03T07:42:34Z 2024 Article 10.1016/j.ejrh.2024.101861 2-s2.0-85195784246 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195784246&doi=10.1016%2fj.ejrh.2024.101861&partnerID=40&md5=ad87e9643b38a449f2cc8a8f7ec2e0df https://irepository.uniten.edu.my/handle/123456789/36468 54 101861 All Open Access; Gold Open Access Elsevier B.V. Scopus |
| spellingShingle | Ladouali S. Katipo?lu O.M. Bahrami M. Kartal V. Sakaa B. Elshaboury N. Keblouti M. Chaffai H. Ali S. Pande C.B. Elbeltagi A. Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition |
| title | Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition |
| title_full | Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition |
| title_fullStr | Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition |
| title_full_unstemmed | Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition |
| title_short | Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition |
| title_sort | short lead time standard precipitation index forecasting: extreme learning machine and variational mode decomposition |
| url_provider | http://dspace.uniten.edu.my/ |