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

Eluã Ramos Coutinho,Ronney Arismel Mancebo Boloy,Angel Ramon Sanchez Delgado,Jonni Guiller Ferreira Madeira,Robson Mariano Da Silva,Pollyanna Rodrigues De Oliveira Dos Santos Coutinho Coutinho

doi:10.1590/0102-7786332013

Abstract

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.

Highlights

Studying climatic processes and atmospheric phenomena may require a large number of data, which are obtained through a set of devices, such as satellites, balloons, radars, sensors and meteorological stations
Analyzing the results presented by measures (r), root-mean-square error (RMSE), mean absolute error (MAE), (D) and (C) in Table 6, it can be seen that the Multilayer Perceptron (MLP) model was superior in all the estimates in comparison to the other models
This fact can be verified by comparing the results of the measurement of error (MPE) obtained by MLP in its estimates, where it remained between 23% and 35% more accurate than MD and between 12% and 18% more accurate Than Multiple Linear Regression (MLR)

Summary

Introduction

Studying climatic processes and atmospheric phenomena may require a large number of data, which are obtained through a set of devices, such as satellites, balloons, radars, sensors and meteorological stations. These devices operate in a constant-collection regime, obtaining data in various time periods such as minutes, hours, days or months, and generate a large volume. These data have great value, both historical and for governmental organizations, private companies and academic institutions. According to Wanderley et al (2014), the lack of a continuous series of climatological data may limit the understanding of the spatial and temporal variability of various meteorological and hydrological processes, and damages the characterization of the climate of a region

Methods

Results

Conclusion