Rainfall intensity classification in the East Coast of Malaysia using discriminant analysis

Mohamad Ameer Imran Mohd Noor, . and Muhammad Afiq Halek, . and Azwan Faiz Lim Muhammad Razwan Lim, . and Hasfazilah Ahmat, . (2023) Rainfall intensity classification in the East Coast of Malaysia using discriminant analysis. Journal of Sustainability Science and Management (Malaysia), 18 (7). pp. 87-102. ISSN 2672-7226

In the previous study, principal component analysis and cluster analysis were used but no information on factors, contribution and classification for rainfall were provided. The logistic regression was not suitable for the rainfall classification since it only works well if the target variable is in binary output. This paper discusses the classification of rainfall based on the contribution of several factors, namely temperature, humidity, wind direction and wind speed on the east coast of Peninsular Malaysia using discriminant analysis. The trend of rainfall intensity was also identified using diurnal variation and Mann Kendall trend test. This study used the data from 2018 to 2020, which covered three locations on the east coast region, Kuala Krai (Kelantan), Kuala Terengganu (Terengganu), and Temerloh (Pahang) furnished by the Malaysian Meteorological Department. There were significant positive relationships among all independent variables, namely, temperature, humidity, wind direction and wind speed, with the rainfall intensity with the significant p-value of Wilk’s Lambda < 0.05. The findings indicated that the classification equation differs from location to location due to different levels of rainfall intensity, the location of monitoring stations and the factors affecting rainfall in these locations.

Full text available from: Official URL: https://jssm.umt.edu.my/wp-content/uploads/2024/01...

Abstract

In the previous study, principal component analysis and cluster analysis were used but no information on factors, contribution and classification for rainfall were provided. The logistic regression was not suitable for the rainfall classification since it only works well if the target variable is in binary output. This paper discusses the classification of rainfall based on the contribution of several factors, namely temperature, humidity, wind direction and wind speed on the east coast of Peninsular Malaysia using discriminant analysis. The trend of rainfall intensity was also identified using diurnal variation and Mann Kendall trend test. This study used the data from 2018 to 2020, which covered three locations on the east coast region, Kuala Krai (Kelantan), Kuala Terengganu (Terengganu), and Temerloh (Pahang) furnished by the Malaysian Meteorological Department. There were significant positive relationships among all independent variables, namely, temperature, humidity, wind direction and wind speed, with the rainfall intensity with the significant p-value of Wilk’s Lambda < 0.05. The findings indicated that the classification equation differs from location to location due to different levels of rainfall intensity, the location of monitoring stations and the factors affecting rainfall in these locations.

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Item Type:	Article
AGROVOC Term:	rain
AGROVOC Term:	meteorology
AGROVOC Term:	climate change
AGROVOC Term:	data collection
AGROVOC Term:	monitoring and evaluation
AGROVOC Term:	meteorologists
AGROVOC Term:	environmental impact assessment
AGROVOC Term:	environmental sciences
Geographical Term:	Malaysia
Depositing User:	Nor Hasnita Abdul Samat
Date Deposited:	23 Nov 2025 05:02
Last Modified:	23 Nov 2025 05:02
URI:	http://webagris.upm.edu.my/id/eprint/1578

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