The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network
The rainfall occurrences are triggered by different types of climate sources not restricted to past precipitation values but may include climate indices such as El Nino/Southern Oscillation, Indian Ocean Dipole, and Madden Julian Oscillation. In this paper, we investigated the effectiveness of assim...
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| Format: | Article |
| Language: | English |
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Publishing Technology
2017
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| Online Access: | http://umpir.ump.edu.my/id/eprint/27001/ http://umpir.ump.edu.my/id/eprint/27001/1/The%20assimilation%20of%20multi-type%20information%20for%20seasonal%20.pdf |
| _version_ | 1848822679470080000 |
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| author | Junaida, Sulaiman Noorhuzaimi@Karimah, Mohd Noor Suryanti, Awang |
| author_facet | Junaida, Sulaiman Noorhuzaimi@Karimah, Mohd Noor Suryanti, Awang |
| author_sort | Junaida, Sulaiman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The rainfall occurrences are triggered by different types of climate sources not restricted to past precipitation values but may include climate indices such as El Nino/Southern Oscillation, Indian Ocean Dipole, and Madden Julian Oscillation. In this paper, we investigated the effectiveness of assimilating two sources of inputs for heavy precipitation forecasting using modular neural network. The assimilated input was obtained by merging two input variable sources (climate indices and precipitation records) according to their individual weighting factor determined by correlation test. To simulate the hydrologic response using merged product, a modular neural network model was developed. The modular concept was implemented by separating the precipitation events based on seasonal monsoon and trained the subset of seasonal data using modular neural network. Four subsets of monthly precipitation data were sampled to evaluate modular neural network model at 1-month lead-time with single precipitation neural network model and multiple linear regression as benchmark models. The results show that the merging method can effectively assimilate information from two sources of inputs to improve the accuracy of heavy precipitation forecasting. |
| first_indexed | 2025-11-15T02:45:04Z |
| format | Article |
| id | ump-27001 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:45:04Z |
| publishDate | 2017 |
| publisher | Publishing Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-270012024-01-04T03:49:08Z http://umpir.ump.edu.my/id/eprint/27001/ The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network Junaida, Sulaiman Noorhuzaimi@Karimah, Mohd Noor Suryanti, Awang QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering The rainfall occurrences are triggered by different types of climate sources not restricted to past precipitation values but may include climate indices such as El Nino/Southern Oscillation, Indian Ocean Dipole, and Madden Julian Oscillation. In this paper, we investigated the effectiveness of assimilating two sources of inputs for heavy precipitation forecasting using modular neural network. The assimilated input was obtained by merging two input variable sources (climate indices and precipitation records) according to their individual weighting factor determined by correlation test. To simulate the hydrologic response using merged product, a modular neural network model was developed. The modular concept was implemented by separating the precipitation events based on seasonal monsoon and trained the subset of seasonal data using modular neural network. Four subsets of monthly precipitation data were sampled to evaluate modular neural network model at 1-month lead-time with single precipitation neural network model and multiple linear regression as benchmark models. The results show that the merging method can effectively assimilate information from two sources of inputs to improve the accuracy of heavy precipitation forecasting. Publishing Technology 2017-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27001/1/The%20assimilation%20of%20multi-type%20information%20for%20seasonal%20.pdf Junaida, Sulaiman and Noorhuzaimi@Karimah, Mohd Noor and Suryanti, Awang (2017) The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network. Advanced Science Letters, 23 (11). pp. 11365-11368. ISSN 1936-6612. (Published) https://doi.org/10.1166/asl.2017.10284 https://doi.org/10.1166/asl.2017.10284 |
| spellingShingle | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Junaida, Sulaiman Noorhuzaimi@Karimah, Mohd Noor Suryanti, Awang The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network |
| title | The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network |
| title_full | The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network |
| title_fullStr | The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network |
| title_full_unstemmed | The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network |
| title_short | The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network |
| title_sort | assimilation of multi-type information for seasonal precipitation forecasting using modular neural network |
| topic | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/27001/ http://umpir.ump.edu.my/id/eprint/27001/ http://umpir.ump.edu.my/id/eprint/27001/ http://umpir.ump.edu.my/id/eprint/27001/1/The%20assimilation%20of%20multi-type%20information%20for%20seasonal%20.pdf |