Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.  ..

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The main objective of this study is to identify the spatiotemporal rainfall patterns using Robust Principal Component Analysis and Fuzzy C-means (RPCA-FCM) of torrential rainfall of the East Coast of Peninsular Malaysia. As a methodology, the RPCA-FCM model was proposed to solve issues in identifying torrential rainfall. Generally, most rainfall data were missing for various reasons. The missing data mechanism was identified to choose suitable imputation methods. RF-MLR was chosen as the best imputation method in handling missing rainfall data. Dimension reduction method coupled with clustering approach was applied to reduce the data dimensions and perform the cluster partition. An RPCA-based Tukey’s biweight correlation and the optimum breakdown point to extract the number of components in RPCA were proposed. The data used in this study was generated using Monte Carlo simulation to evaluate the performance of the proposed statistical model. The result revealed that a bre.....

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