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In this paper, we propose a hybrid model that combines wavelet transform and artificial neural networks (ANN) to forecast the time series of hourly average methane (CH4) concentration. In this work, we want to improve the accuracy of predicting the dynamics of changes in the surface concentration of CH4. The model is based on data from environmental monitoring of greenhouse gases on the Belyy Island of the Yamal-Nenets Autonomous Okrug, Russia. The initial data for building the proposed model were obtained in the period July–August 2017. The time series of the CH4 concentration was decomposed using a discrete wavelet transform into five components—one approximating and four detailing components. These five components, together with synchronized series of meteorological parameters (temperature, humidity, and pressure), were used to train ten ANNs—five autoregressive networks with exogenous input (NARX) and five recurrent neural networks (Elman). The forecast was calculated as the sum of the forecasts for each of the five components. The forecast accuracy was assessed using several indices and a Taylor chart. The hybrid approach improved the accuracy of the forecasts by more than 20%. The hybrid model based on the NARX shown the best accuracy. [ABSTRACT FROM AUTHOR]

This paper compares the most applicable models for the forecasting of time series. Models based on artificial neural networks nonlinear autoregressive neural network with external input (NARX), Elman's neural network, and vector autoregression model were implemented. A special algorithm based on the analysis and prediction of the model residuals, which significantly improves the accuracy of the forecast, was proposed. For the prediction, we used the data of the main greenhouse gases methane (CH4) and water vapor (H2O) concentrations measured in summer 2016 in the surface layer of the atmospheric air on the Arctic island Belyy, Russia. The time interval of 192 h was chosen. The time interval was characterized by the significant daily variations in the CH4 concentration; the H2O concentrations did not have a pronounced trend. Values corresponding to the first 168 h of the interval were used for ANN training, and then, concentrations were predicted for the next 24 h. The accuracy of the prediction was determined by the set of errors and indices. The NARX model was more accurate than models based on Elman network and the VAR. The accuracy gain was from 16.5 to 40% for the models, which predicted the CH4 concentration, and from 21 to 58% for the models, which predicted the H2O concentration. The application of the proposed combined approach made it possible to increase the accuracy of the base model from 1.5 to 20% for the CH4 and from 4 to 20% for the H2O (depending on the corresponding errors and indices). The presented Taylor diagram was also showed the advantage of the proposed approach. [ABSTRACT FROM AUTHOR]

Disinformation attacks that make use of social media platforms, e.g., the attacks orchestrated by the Russian "Internet Research Agency" during the 2016 U.S. Presidential election campaign and the 2016 Brexit referendum in the UK, have led to increasing demands from governmental agencies for AI tools that are capable of identifying such attacks in their earliest stages, rather than responding to them in retrospect. This research undertaken on behalf of the Canadian Armed Forces and Department of National Defence. Our ultimate objective is the development of an integrated set of machine-learning algorithms which will mobilize artificial intelligence to identify hostile disinformation activities in "near-real-time." Employing The Dark Crawler, the Posit Toolkit, TensorFlow (Deep Neural Networks), plus the Random Forest classifier and short-text classification programs known as LibShortText and LibLinear, we have analysed a wide sample of social media posts that exemplify the "fake news" that was disseminated by Russia's Internet Research Agency, comparing them to "real news" posts in order to develop an automated means of classification. [ABSTRACT FROM AUTHOR]