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Rainfall forecasting using the group method of data handling model: A case study of Sarawak, Malaysia

Time series forecasting has led to the emergence of various forecasting models applied to arrays of time series problems, such as rainfall forecasting, dengue forecasting, tourism forecasting, and others. The Artificial Neural Network (ANN) is a popular Artificial Intelligence (AI) model extensively...

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Main Authors:	Narawi, Azlina, Abang Jawawi, Dayang Norhayati, Samsudin, Ruhaidah
Format:	Article
Published:	Springer Science and Business Media Deutschland GmbH 2022
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/100097/ http://dx.doi.org/10.1007/978-3-030-98741-1_12
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id	my.utm.100097
record_format	eprints
spelling	my.utm.1000972023-03-29T06:38:17Z http://eprints.utm.my/id/eprint/100097/ Rainfall forecasting using the group method of data handling model: A case study of Sarawak, Malaysia Narawi, Azlina Abang Jawawi, Dayang Norhayati Samsudin, Ruhaidah QA75 Electronic computers. Computer science Time series forecasting has led to the emergence of various forecasting models applied to arrays of time series problems, such as rainfall forecasting, dengue forecasting, tourism forecasting, and others. The Artificial Neural Network (ANN) is a popular Artificial Intelligence (AI) model extensively employed in much research for time series forecasting due to its nonlinear modeling ability. The group method of data handling (GMDH) is an AI model with the characteristics of heuristic self-organizing capability. This model has shown successful results in many areas. Nowadays, rainfall forecasting remains a vital interest and is still actively researched, where researchers use different soft computing techniques. The ANN has been popularly studied for rainfall forecasting because of its ability to efficiently train a large amount of data and completely detect complex connections between nonlinear dependent and independent variables. However, research on rainfall forecasting using the GMDH model is limited. Hence, this paper designates the GMDH model and its application to rainfall forecasting. The conventional GMDH model uses the polynomial transfer function. The sigmoid transfer function is proven to solve the multicollinearity issue caused by the quadratic polynomial of the GMDH model. Hence, this research tackled the multicollinearity issue of using different transfer functions in GMDH modeling and forecasting. The study compares the results of using polynomial and sigmoid transfer functions for the GMDH model development. This research uses the Malaysia rainfall dataset of the Sarawak regions from 2010 until 2019 as a case study to evaluate the effectiveness of the GMDH models in this research. The results exhibit that the polynomial transfer function is dominant in achieving the smallest RMSE and MSE values in all regions. Springer Science and Business Media Deutschland GmbH 2022 Article PeerReviewed Narawi, Azlina and Abang Jawawi, Dayang Norhayati and Samsudin, Ruhaidah (2022) Rainfall forecasting using the group method of data handling model: A case study of Sarawak, Malaysia. Lecture Notes on Data Engineering and Communications Technologies, 127 (NA). pp. 129-140. ISSN 2367-4512 http://dx.doi.org/10.1007/978-3-030-98741-1_12 DOI : 10.1007/978-3-030-98741-1_12
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Narawi, Azlina Abang Jawawi, Dayang Norhayati Samsudin, Ruhaidah Rainfall forecasting using the group method of data handling model: A case study of Sarawak, Malaysia
description	Time series forecasting has led to the emergence of various forecasting models applied to arrays of time series problems, such as rainfall forecasting, dengue forecasting, tourism forecasting, and others. The Artificial Neural Network (ANN) is a popular Artificial Intelligence (AI) model extensively employed in much research for time series forecasting due to its nonlinear modeling ability. The group method of data handling (GMDH) is an AI model with the characteristics of heuristic self-organizing capability. This model has shown successful results in many areas. Nowadays, rainfall forecasting remains a vital interest and is still actively researched, where researchers use different soft computing techniques. The ANN has been popularly studied for rainfall forecasting because of its ability to efficiently train a large amount of data and completely detect complex connections between nonlinear dependent and independent variables. However, research on rainfall forecasting using the GMDH model is limited. Hence, this paper designates the GMDH model and its application to rainfall forecasting. The conventional GMDH model uses the polynomial transfer function. The sigmoid transfer function is proven to solve the multicollinearity issue caused by the quadratic polynomial of the GMDH model. Hence, this research tackled the multicollinearity issue of using different transfer functions in GMDH modeling and forecasting. The study compares the results of using polynomial and sigmoid transfer functions for the GMDH model development. This research uses the Malaysia rainfall dataset of the Sarawak regions from 2010 until 2019 as a case study to evaluate the effectiveness of the GMDH models in this research. The results exhibit that the polynomial transfer function is dominant in achieving the smallest RMSE and MSE values in all regions.
format	Article
author	Narawi, Azlina Abang Jawawi, Dayang Norhayati Samsudin, Ruhaidah
author_facet	Narawi, Azlina Abang Jawawi, Dayang Norhayati Samsudin, Ruhaidah
author_sort	Narawi, Azlina
title	Rainfall forecasting using the group method of data handling model: A case study of Sarawak, Malaysia
title_short	Rainfall forecasting using the group method of data handling model: A case study of Sarawak, Malaysia
title_full	Rainfall forecasting using the group method of data handling model: A case study of Sarawak, Malaysia
title_fullStr	Rainfall forecasting using the group method of data handling model: A case study of Sarawak, Malaysia
title_full_unstemmed	Rainfall forecasting using the group method of data handling model: A case study of Sarawak, Malaysia
title_sort	rainfall forecasting using the group method of data handling model: a case study of sarawak, malaysia
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2022
url	http://eprints.utm.my/id/eprint/100097/ http://dx.doi.org/10.1007/978-3-030-98741-1_12
_version_	1762392168135131136
score	13.211869

Rainfall forecasting using the group method of data handling model: A case study of Sarawak, Malaysia

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