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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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Main Authors: Junaida, Sulaiman, Noorhuzaimi@Karimah, Mohd Noor, Suryanti, Awang
Format: Article
Language:English
Published: Publishing Technology 2017
Subjects:
QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/27001/1/The%20assimilation%20of%20multi-type%20information%20for%20seasonal%20.pdf
http://umpir.ump.edu.my/id/eprint/27001/
https://doi.org/10.1166/asl.2017.10284
https://doi.org/10.1166/asl.2017.10284
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spelling my.ump.umpir.270012020-03-10T08:16:18Z 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 https://doi.org/10.1166/asl.2017.10284 https://doi.org/10.1166/asl.2017.10284
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
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
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.
format Article
author Junaida, Sulaiman
Noorhuzaimi@Karimah, Mohd Noor
Suryanti, Awang
author_facet Junaida, Sulaiman
Noorhuzaimi@Karimah, Mohd Noor
Suryanti, Awang
author_sort Junaida, Sulaiman
title 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_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_sort assimilation of multi-type information for seasonal precipitation forecasting using modular neural network
publisher Publishing Technology
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/27001/1/The%20assimilation%20of%20multi-type%20information%20for%20seasonal%20.pdf
http://umpir.ump.edu.my/id/eprint/27001/
https://doi.org/10.1166/asl.2017.10284
https://doi.org/10.1166/asl.2017.10284
_version_ 1662754752591560704
score 13.211869

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