Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series

Abstract This study estimates and fills real flaws in a series of meteorological data belonging to four regions of the state of Rio de Janeiro. For this, an Artificial Neural Network (ANN) of Multilayer Perceptron (MLP) was applied. In order to evaluate its adequacy, the monthly variables of maximum air temperature and relative humidity of the period between 05/31/2002 and 12/31/2014 were estimated and compared with the results obtained by Multiple Linear Regression (MLR) and Regions Average (RA), and still faced with the recorded data. To analyze the estimated values and define the best model for filling, statistical techniques were applied such as correlation coefficient (r), Mean Percentage Error (MPE) and others. The results showed a high relation with the recorded data, presenting indexes between 0.94 to 0.98 of (r) for maximum air temperature and between 2.32% to 1.05% of (MPE), maintaining the precision between 97% A 99%. For the relative air humidity, the index (r) with MLP remained between 0.77 and 0.94 and (MPE) between 2.41% and 1.85%, maintaining estimates between 97% and 98%. These results highlight MLP as being effective in estimating and filling missing values.

Bibliographic Details

Main Authors:	Coutinho,Eluã Ramos, Silva,Robson Mariano da, Madeira,Jonni Guiller Ferreira, Coutinho,Pollyanna Rodrigues de Oliveira dos Santos, Boloy,Ronney Arismel Mancebo, Delgado,Angel Ramon Sanchez
Format:	Digital revista
Language:	English
Published:	Sociedade Brasileira de Meteorologia 2018
Online Access:	http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862018000200317
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