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Imputation of missing values in a precipitation-runoff process database

Kalteh, Aman Mohammad LU and Hjorth, Peder LU (2009) In Hydrology Research 40(4). p.420-432
Abstract
Hydrologists are often faced with the problem of missing values in a precipitation-runoff process database to construct runoff prediction models. They tend to use simple and naive methods to deal with the problem of missing data. Thus far, the common practice has been to discard observations with missing values. In this paper, we present some statistically principled methods for gap filling and discuss the pros and cons of these methods. We employ and discuss imputations of missing values by means of self-organizing map (SOM), multilayer perceptron (MLP), multivariate nearest-neighbor (MNN), regularized expectation-maximization algorithm (REGEM) and multiple imputation (MI) in the context of a precipitation-runoff process database in... (More)
Hydrologists are often faced with the problem of missing values in a precipitation-runoff process database to construct runoff prediction models. They tend to use simple and naive methods to deal with the problem of missing data. Thus far, the common practice has been to discard observations with missing values. In this paper, we present some statistically principled methods for gap filling and discuss the pros and cons of these methods. We employ and discuss imputations of missing values by means of self-organizing map (SOM), multilayer perceptron (MLP), multivariate nearest-neighbor (MNN), regularized expectation-maximization algorithm (REGEM) and multiple imputation (MI) in the context of a precipitation-runoff process database in northern Iran in order to construct a serially complete database for analyses such as runoff prediction. In our case, the SOM and MNN tend to give similar and robust results. REGEM and MI build on the assumption of multivariate normal data, which we don't seem to have in one of our cases. MLP tends to produce inferior results because it fragments the data into 68 different models. Therefore, we conclude that it makes most sense to use either the computationally simple MNN method or the more demanding SOM. (Less)
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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
values, missing, MI, REGEM, MNN, MLP, data fill in, imputation methods: SOM, serially complete data
in
Hydrology Research
volume
40
issue
4
pages
420 - 432
publisher
IWA Publishing
external identifiers
  • wos:000267568500006
  • scopus:67651163465
ISSN
1998-9563
DOI
10.2166/nh.2009.001
language
English
LU publication?
yes
id
ce578f48-019c-4676-96da-671fd5937157 (old id 1463185)
date added to LUP
2016-04-01 12:00:54
date last changed
2025-04-04 13:56:45
@article{ce578f48-019c-4676-96da-671fd5937157,
  abstract     = {{Hydrologists are often faced with the problem of missing values in a precipitation-runoff process database to construct runoff prediction models. They tend to use simple and naive methods to deal with the problem of missing data. Thus far, the common practice has been to discard observations with missing values. In this paper, we present some statistically principled methods for gap filling and discuss the pros and cons of these methods. We employ and discuss imputations of missing values by means of self-organizing map (SOM), multilayer perceptron (MLP), multivariate nearest-neighbor (MNN), regularized expectation-maximization algorithm (REGEM) and multiple imputation (MI) in the context of a precipitation-runoff process database in northern Iran in order to construct a serially complete database for analyses such as runoff prediction. In our case, the SOM and MNN tend to give similar and robust results. REGEM and MI build on the assumption of multivariate normal data, which we don't seem to have in one of our cases. MLP tends to produce inferior results because it fragments the data into 68 different models. Therefore, we conclude that it makes most sense to use either the computationally simple MNN method or the more demanding SOM.}},
  author       = {{Kalteh, Aman Mohammad and Hjorth, Peder}},
  issn         = {{1998-9563}},
  keywords     = {{values; missing; MI; REGEM; MNN; MLP; data fill in; imputation methods: SOM; serially complete data}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{420--432}},
  publisher    = {{IWA Publishing}},
  series       = {{Hydrology Research}},
  title        = {{Imputation of missing values in a precipitation-runoff process database}},
  url          = {{http://dx.doi.org/10.2166/nh.2009.001}},
  doi          = {{10.2166/nh.2009.001}},
  volume       = {{40}},
  year         = {{2009}},
}