Missing Data Imputation Through SGTM Neural-Like Structure for Environmental Monitoring Tasks

The article describes a new missing data imputation method. It is based on the use of a high-speed neural-like structure of the Successive Geometric Transformations Model. The importance of the research is based on the analysis of disadvantages of the known methods for missing data processing. Various simple and complex algorithms are analyzed, among which are the arithmetic mean algorithm, regression modeling, etc. It is shown that the above-mentioned imputation methods in data monitoring of air pollution do not allow to obtain reliable results due to the low prediction accuracy. An effective method for processing data imputation through SGTM neural-like structure is proposed. An example of filling data by forecasting CO, NO and NO2 missed parameters in data monitoring of air pollution is given. A comparison of the proposed method with the arithmetic mean algorithm is carried out. Accuracies of the data imputation by developed method and by arithmetic mean algorithm are based on calculated evaluation criteria: Root mean squared errors. Experimentally established that the data imputation method through SGTM neural-like structure has a three times higher accuracy of the data imputation than the arithmetic mean algorithm. The proposed approach can be used in various areas such as medicine, materials science, economics, science services, etc.