MANNGA: A Robust Method for Gap Filling Meteorological Data

Abstract

This paper presents Mannga (Multiple variables with Artificial Neural Network and Genetic Algorithm), a method designed for gap filling meteorological data. The main approach is to estimate the missing data based on values of other meteorological variables measured at the same time in the same local, since the meteorological variables are strongly related. Experimental tests showed the performance of Mannga compared with other two methods typically used by researches in this area. Good results were achieved, with high accuracy even for sequential failures, which is a big challenge for researchers. The core advantages of Mannga are the flexibility of handling different types of meteorological data, the ability of select the best variables to assist the gap filling and the capacity to deal with sequential failures. Moreover, the method is available to public use with the Java programming language.

Uso de árvore de decisão para escolha de método de preenchimento de falhas em dados meteorológicos

“…Em [Correia et al 2016]é descrito o uso de redes neurais artificiais no preenchimento de falhas em séries de precipitação mensal. Já em [Ventura et al 2019]é apresentado um método de Inteligencia Artificial para preencher falhas em dados meteorológicos, do mesmo modo que [Júnior et al 2020] utiliza Inteligencia Artificial para modelar dados meteorológicos. O trabalho de [Rangel et al 2018] faz uso de transformadas de Fourier no preenchimento de falhas em dados de intensidade do vento.…”