Filling missing meteorological data with Computational Intelligence methods

Estimates of temperature and humidity values at a specific time of day, from hourly to monthly profiles, are needed for a number of environmental, ecological, agricultural and technical applications, ranging from natural hazards assessments, crop growth forecasting to designing solar energy systems. In climatology, they constitute the basis for drawing conclusions about climate variability. Data used in such analyses should be complete and reliable. Therefore, effective methods for filling missing values are sought. The initial scope of this research is to investigate the efficiency of computational intelligence methods in filling missing daily temperature and humidity parameters values. For this reason, a number of experiments have been conducted with Artificial Neural Networks and Support Vector Regression using meteorological data from the city of Wroclaw in Poland. The performance of these methods has been evaluated using standard statistical indicators, such as Correlation Coefficient and Root Mean Squared Error. Finally, certain computational intelligence techniques are proposed that can be used to predict daily temperature and humidity values more accurately in order to fill the missing data