Abstract
Ozone is one of the pollutants with most negative effects on human health and in general on the biosphere. Many data-acquisition networks collect data about ozone values in both urban and background areas. Usually, these data are incomplete or corrupt and the imputation of the missing values is a priority in order to obtain complete datasets, solving the uncertainty and vagueness of existing problems to manage complexity. In the present paper, multiple-regression techniques and Artificial Neural Network models are applied to approximate the absent ozone values from five explanatory variables containing air-quality information. To compare the different imputation methods, real-life data from six data-acquisition stations from the region of Castilla y León (Spain) are gathered in different ways and then analyzed. The results obtained in the estimation of the missing values by applying these techniques and models are compared, analyzing the possible causes of the given response.
Highlights
Introduction and Related WorkThe ozone (O3) is an odorless, colorless, and highly reactive gas composed of three oxygen atoms
The results suggested that SOM and MLBP are the methods of choice for air-quality data imputation and even better results can be achieved by using the Multiple imputation (MI)
The high values of Standard Deviation (STD) for the runtime in the case of Radial-Basis-Function Networks (RBFN) are due to the fact that it greatly varies from one fold to the others
Summary
The ozone (O3) is an odorless, colorless, and highly reactive gas composed of three oxygen atoms It is formed both in the Earth’s upper atmosphere (stratospheric ozone) and at ground level (tropospheric ozone). (1) MLR and MN-LR attained very similar results in terms of MSE and execution time. These results are slightly worse than those obtained by the two ANN models (RBFN and MLP). (2) In the case of RBFN, slight differences have been obtained when varying the number of neurons in the hidden layer, in terms of both the MSE and the execution time. As in the previous case (RBFN), the best results are obtained for the WD and the worst results for the SD, with small differences between the results in the three datasets. The results obtained by MLP improve those obtained by RBFN, only when applying the LM training algorithm
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