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/ 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

id	ump-27001
recordtype	eprints
spelling	ump-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
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
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/ 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
first_indexed	2023-09-18T22:42:21Z
last_indexed	2023-09-18T22:42:21Z
_version_	1777416999356334080

The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network

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