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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • 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

  • 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