Imputation of missing values in a precipitation–runoff process database

Kalteh, Aman Mohammad; Hjorth, P

doi:10.2166/nh.2009.001

Cited by 67 publications

(44 citation statements)

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“…Previous studies [9,17,18] have reported as well that the self-organizing map (SOM), an unsupervised ANN, showed satisfactory imputation results. These nonlinear models have demonstrated their performances by showing better imputation results than the traditional statistical methods [19].…”

Section: Introductionmentioning

confidence: 96%

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Kim

Baek

Ligaray

et al. 2015

Water

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Faulty field sensors cause unreliability in the observed data that needed to calibrate and assess hydrology models. However, it is illogical to ignore abnormal or missing values if there are limited data available. This study addressed this problem by applying data imputation to replace incorrect values and recover missing streamflow information in the dataset of the Samho gauging station at Taehwa River (TR), Korea from 2004 to 2006. Soil and Water Assessment Tool (SWAT) and two machine learning techniques, Artificial Neural Network (ANN) and Self Organizing Map (SOM), were employed to estimate streamflow using reasonable flow datasets of Samho station from 2004 to 2009. The machine learning models were generally better at capturing high flows, while SWAT was better at simulating low flows.

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Section: Introductionmentioning

confidence: 96%

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Kim

Baek

Ligaray

et al. 2015

Water

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“…The process repeats in this way until all the missing entries of b are imputed. Variants of this method are considered in [86], [143], and [281].…”

Section: Best Neighbor Imputingmentioning

confidence: 99%

Imputing Missing Entries of a Data Matrix: A Review

Dax¹

2014

JAC

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The problem of imputing missing entries of a data matrix is easy to state: Some entries of the matrix are unknown and we want to assign "appropriate values" to these entries. The need for solving such problems arises in several applications, ranging from traditional ields to modern ones. Typical examples of traditional ields are statistical analysis of incomplete survey data, business reports, operations management, psychometrika, meteorology and hydrology. Modern applications arise in machine learning, data mining, DNA microarrays data, chemometrics, computer vision, recommender systems and collaborative iltering. The problem is highly interesting and challenging. Many ingenious algorithms have been proposed, and there is vast literature on imputing techniques. Yet, most of the papers consider the imputing problem within the context of a speci ic application or a speci ic approach. The current survey provides a broad view of the problem, one that exposes the large variety of imputing methods. Old and new methods are examined with focus on the ideas behind the methods. It is illustrated how simple ideas evolve into highly sophisticated algorithms. The review enables us to identify a number of basic concepts and tools which are shared by several imputing methods. Equivalence theorems that we prove reveal surprising relations between apparently different methods.

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“…Next, the SOM will search the best matching unit (BMU) by computing the Euclidean distance to the input vector. (2) where W is weight vector. The determination of the node that are within the BMU`s neighbourhood is made by calculating the radius of the neighbourhood, σ(t) and it will decrease over time as…”

Section: Basic Of Self-organizing Mapmentioning

confidence: 99%

“…Kalteh and Hjorth (2009) used SOM to construct a complete database of runoff prediction in Northern Iran. Multiple imputation, multivariate nearest neighbour, multilayer perceptron (MLP) and means of SOM are the imputation methods used to estimate the missing data.…”

Section: Introductionmentioning

confidence: 99%

Application of Self-Organizing Map (SOM) in Missing Daily Rainfall Data in Malaysia

Kang¹,

Yusof²

2012

IJCA

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Self-organizing map (SOM) is applied to deal with missing daily rainfall data with different rainfall patterns in Peninsular Malaysia. In this study, stations from Damansara and Kelantan are focused and aimed to evaluate the effectiveness of SOM in clustering and imputation of missing data. The missing data that are imputed by SOM are evaluated by computing the mean square error (MSE) and coefficient correlation(R). Besides, the effects of the imputed data to the mean and variance of the rainfall data is also been observed. The clustering analysis showed that all the stations in Damansara are grouped distinctively, and having a good and even distribution of rain intensity as compared to Kelantan. Meanwhile it is also found that SOM is an excellent tool in estimation of missing data.

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Imputation of missing values in a precipitation–runoff process database

Cited by 67 publications

References 0 publications

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Imputing Missing Entries of a Data Matrix: A Review

Application of Self-Organizing Map (SOM) in Missing Daily Rainfall Data in Malaysia

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