Infilling missing rainfall and runoff data for Sarawak, Malaysia using gaussian mixture model based K-nearest neighbor imputation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Infilling missing rainfall and runoff data for Sarawak, Malaysia using gaussian mixture model based K-nearest neighbor imputation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Infilling missing rainfall and runoff data for Sarawak, Malaysia using gaussian mixture model based K-nearest neighbor imputation

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

—

Počet stran výsledku

12

Strana od-do

27-38

Název nakladatele

Springer Verlag

Místo vydání

Berlin

Místo konání akce

Gratz

Datum konání akce

9. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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