Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network
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 dat...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
UNIR - Universidad Internacional de La Rioja
2021
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/46218/1/ijimai6_7_4.pdf http://ir.unimas.my/id/eprint/46218/ https://www.ijimai.org/journal/bibcite/reference/3001 |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | 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 pre-processing mechanism for non-precipitation data by using principal component analysis
(PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data.
The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and
then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is
presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function
fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SCFFNN), feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of
mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four
imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing
meteorological data into an understandable format for the missing data imputation. |
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