The study of forecasting methods in the tasks of analyzing environmental monitoring data
The article considers to provide short-time forecasts of environmental pollution by methane emissions for environmental monitoring system’s data. The study aims to develop a methodology for solving the problem of accurate prediction of the air pollution level. The authors performed comparative analysis of the following models : autoregressive integrated moving average (ARIMA) model and Long short-term memory (LSTM) artificial recurrent neural network (RNN) model. Python-based solution was used for analysis and visualisation. Statsmodels module was used for statistical models estimation. Pytorch library was used for the neural networks development. The results of the study. The ensemble methods is suggested as prospective approach for providing short-time forecast. Two-versioned adoptive models system can be an integral part of decision support system in ecological monitoring complex system. Future research directions. The developed methodology may be extended to different types of data analysis tasks in the field of environmental monitoring as a part of complex solution.