A comparative study of different imputation methods for daily rainfall data in east-coast Peninsular Malaysia

Siti Mariana Che Mat Nor (Universiti Pendidikan Sultan Idris)
Shazlyn Milleana Shaharudin (Universiti Pendidikan Sultan Idris)
Shuhaida Ismail (Universiti Tun Hussein Onn Malaysia)
Nurul Hila Zainuddin (Universiti Pendidikan Sultan Idris)
Mou Leong Tan (Universiti Sains Malaysia)

Bulletin of Electrical Engineering and Informatics
Vol 9, No 2: April 2020

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.