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Type :article
Subject :G Geography (General)
Main Author :Siti Mariana Che Mat Nor
Additional Authors :Shazlyn Milleana Shaharudin
Shuhaida Ismail
Nurul Hila Zainuddin
Tan, Mou Leong
Title :A comparative study of different imputation methods for daily rainfall data in East-Coast Peninsular Malaysia
Place of Production :Tanjong Malim
Publisher :Fakulti Sains dan Matematik
Year of Publication :2020
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Full Text :Click to view PDF file

Abstract : Universiti Pendidikan Sultan Idris
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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