02361nas a2200277   4500000000100000000000100001008004100002260001200043653001500055653002600070653003300096653003100129653001800160100001700178700001600195700001900211700001900230700002700249700001900276245008600295856007900381300001000460490000600470520159300476022001402069       2021                            d  c09/202110aImputation10aMissing Rainfall Data10aPrincipal Component Analysis10aSine Cosine Neural Network10aDeep Learning1 aPo Chan Chiu1 aAli Selamat1 aOndrej Krejcar1 aKing Kuok Kuok1 aEnrique Herrera-Viedma1 aGiuseppe Fenza00aImputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network  uhttps://www.ijimai.org/journal/sites/default/files/2021-08/ijimai6_7_4.pdf  a39-480 v63 aMissing 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.  a1989-1660