Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network Chan Chiu, Po Herrera Viedma, Enrique Imputation Missing Rainfall Data Principal component analysis (PCA) Sine cosine neural network Deep learning The authors would like to acknowledge the Malaysian Meteorological Department and Department of Irrigation and Drainage (DID), Sarawak, Malaysia, for providing the meteorological and rainfall data in this study. This work was supported/funded by the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1). The authors sincerely thank Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876, and SLAI supported under Ministry of Higher Education Malaysia for the completion of the research. The work is partially supported by the SPEV project (ID: 2102-2021), Faculty of Informatics and Management, University of Hradec Kralove. We are also grateful for the support of Ph.D. students Michal Dobrovolny and Sebastien Mambou in consultations regarding application aspects from Hradec Kralove University, Czech Republic. The APC was funded by the SPEV project 2102/2021, Faculty of Informatics and Management, University of Hradec Kralove. 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. 2022-03-30T08:35:13Z 2022-03-30T08:35:13Z 2021-08-12 info:eu-repo/semantics/article Chiu, P. C... [et al.] (2021). Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network. International Journal of Interactive Multimedia & Artificial Intelligence, 6(7). DOI: [10.9781/ijimai.2021.08.013] http://hdl.handle.net/10481/73940 10.9781/ijimai.2021.08.013 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España Universidad Internacional de La Rioja