Imputation of Rainfall Data Using Improved Neural Network Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50017983" target="_blank" >RIV/62690094:18450/21:50017983 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-68799-1_28" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-68799-1_28</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-68799-1_28" target="_blank" >10.1007/978-3-030-68799-1_28</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Imputation of Rainfall Data Using Improved Neural Network Algorithm

Popis výsledku v původním jazyce

Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel input structure for the missing data imputation method. Principal component analysis (PCA) is used to extract the most relevant features from the meteorological data. This paper introduces the combined input of the significant principal components (PCs) and rainfall data from nearest neighbor gauging stations as the input to the estimation of the missing values. Second, the effects of the combination input for infilling the missing rainfall data series were compared using the sine cosine algorithm neural network (SCANN) and feedforward neural network (FFNN). The results showed that SCANN outperformed FFNN imputation in terms of mean absolute error (MAE), root means square error (RMSE) and correlation coefficient (R), with an average accuracy of more than 90%. This study revealed that as the percentage of missingness increased, the precision of both imputation methods reduced. © 2021, Springer Nature Switzerland AG.

Název v anglickém jazyce

Imputation of Rainfall Data Using Improved Neural Network Algorithm

Popis výsledku anglicky

Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel input structure for the missing data imputation method. Principal component analysis (PCA) is used to extract the most relevant features from the meteorological data. This paper introduces the combined input of the significant principal components (PCs) and rainfall data from nearest neighbor gauging stations as the input to the estimation of the missing values. Second, the effects of the combination input for infilling the missing rainfall data series were compared using the sine cosine algorithm neural network (SCANN) and feedforward neural network (FFNN). The results showed that SCANN outperformed FFNN imputation in terms of mean absolute error (MAE), root means square error (RMSE) and correlation coefficient (R), with an average accuracy of more than 90%. This study revealed that as the percentage of missingness increased, the precision of both imputation methods reduced. © 2021, Springer Nature Switzerland AG.

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

2021

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

978-3-030-68798-4

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

17

Strana od-do

390-406

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Cham

Místo konání akce

On-line

Datum konání akce

10. 1. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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