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2021 | OriginalPaper | Buchkapitel

Imputation of Rainfall Data Using Improved Neural Network Algorithm

verfasst von : Po Chan Chiu, Ali Selamat, Ondrej Krejcar, King Kuok Kuok

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

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.

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Metadaten
Titel
Imputation of Rainfall Data Using Improved Neural Network Algorithm
verfasst von
Po Chan Chiu
Ali Selamat
Ondrej Krejcar
King Kuok Kuok
Copyright-Jahr
2021
Buch
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68798-4

Electronic ISBN: 978-3-030-68799-1

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-68799-1_28