Infilling missing rainfall and runoff data for Sarawak, Malaysia using gaussian mixture model based K-nearest neighbor imputation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50015951" target="_blank" >RIV/62690094:18450/19:50015951 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-22999-3_3" target="_blank" >http://dx.doi.org/10.1007/978-3-030-22999-3_3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-22999-3_3" target="_blank" >10.1007/978-3-030-22999-3_3</a>
Alternative languages
Result language
angličtina
Original language name
Infilling missing rainfall and runoff data for Sarawak, Malaysia using gaussian mixture model based K-nearest neighbor imputation
Original language description
Hydrologists are often encountered problem of missing values in a rainfall and runoff database. They tend to use the normal ratio or distance power method to deal with the problem of missing data in the rainfall and runoff database. However, this method is time consuming and most of the time, it is less accurate. In this paper, two neighbor-based imputation methods namely K-nearest neighbor (KNN) and Gaussian mixture model based KNN imputation (GMM-KNN) were explored for gap filling the missing rainfall and runoff database. Different percentage of missing data entries were inserted randomly into the database such as 2%, 5%, 10%, 15% and 20% of missing data. Pros and cons of these two methods were compared and discussed. The selected study area is Bedup Basin, located at Samarahan Division, Sarawak, East Malaysia. It is observed that the GMM-KNN imputation method results in the best estimation accuracy for the missing rainfall and runoff database.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN
978-3-030-22998-6
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
27-38
Publisher name
Springer Verlag
Place of publication
Berlin
Event location
Gratz
Event date
Jul 9, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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