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dc.contributor.authorChan Chiu, Po
dc.contributor.authorHerrera Viedma, Enrique 
dc.date.accessioned2022-03-30T08:35:13Z
dc.date.available2022-03-30T08:35:13Z
dc.date.issued2021-08-12
dc.identifier.citationChiu, 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]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/73940
dc.descriptionThe 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.es_ES
dc.description.abstractMissing 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.es_ES
dc.description.sponsorshipMinistry of Higher Education under the Fundamental Research Grant Scheme FRGS/1/2018/ICT04/UTM/01/1es_ES
dc.description.sponsorshipUniversiti Teknologi Malaysia (UTM) under Ministry of Higher Education Malaysia 20H04es_ES
dc.description.sponsorshipMalaysia Research University Network (MRUN) under Ministry of Higher Education Malaysia 4L876es_ES
dc.description.sponsorshipSLAI under Ministry of Higher Education Malaysiaes_ES
dc.description.sponsorshipSPEV project, Faculty of Informatics and Management, University of Hradec Kralove 2102-2021 2102/2021es_ES
dc.description.sponsorshipHradec Kralove University, Czech Republices_ES
dc.language.isoenges_ES
dc.publisherUniversidad Internacional de La Riojaes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectImputationes_ES
dc.subjectMissing Rainfall Dataes_ES
dc.subjectPrincipal component analysis (PCA)es_ES
dc.subjectSine cosine neural networkes_ES
dc.subjectDeep learninges_ES
dc.titleImputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Networkes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.9781/ijimai.2021.08.013
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España