| Summary: | 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. © Penerbit UMT
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