Comparison of missing value estimation techniques in rainfall data of Bangladesh

  • Farzana JahanEmail author
  • Narayan Chandra Sinha
  • Md. Mahfuzur Rahman
  • Md. Morshadur Rahman
  • Md. Sanaul Haque Mondal
  • M. Ataharul Islam
Original Paper


The presence of missing values in daily rainfall data may hamper the analyses to determine effective results for solving problems of hydrological, agricultural, and climatological issues. The study attempts to select an appropriate method for estimating the missing value of daily rainfall data of Bangladesh. For this purpose, eight methods and seven comparison techniques are employed. For imputation of missing values employing these methods, three sets of daily rainfall data (1, 5, and 10% missing values) with 1000 repetitions are considered randomly for five regions of the country. These samples are artificially created as missing and then imputation for these missing values is made applying the selected methods. The relative performance of the methods are examined using some comparison criteria. The following observations can be made from the study regarding the choice of the appropriate missing value estimation technique: for imputation of the missing values of daily rainfall data, the arithmetic average method for rainfall stations Chittagong and Rajshahi in the south-east region and the north-west region, respectively, is found as the best methods. Further, the single best estimator method for rainfall stations Sylhet and Dhaka in the north-east region and the mid-region, respectively, and the EM-MCMC method for rainfall station Khulna of the south-east region are also identified as the best methods in respect of Kolmogorov-Smirnov test, the lowest bias of estimate, the value of S index, etc.



This study is supported under the HEQEP sub-project, CP-3293, in the Department of Applied Statistics, East West University funded by World Bank and implemented by University Grants Commission of Bangladesh (UGC). The authors are also grateful to Bangladesh Meteorological Department (BMD) for providing the data. We acknowledge the critical comments from anonymous reviewers and editor.


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematical Sciences, Science and Engineering FacultyQueensland University of TechnologyBrisbaneAustralia
  2. 2.Dhaka School of EconomicsDhakaBangladesh
  3. 3.Green Business SchoolGreen University of BangladeshDhakaBangladesh
  4. 4.Department of StatisticsUniversity of DhakaDhakaBangladesh
  5. 5.Tokyo Institute of TechnologyTokyoJapan
  6. 6.Institute of Statistical Research and Training (ISRT)University of DhakaDhakaBangladesh

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