We study intense geomagnetic storms (Dst < -100nT) during the first half of the solar cycle 24. This type of storm appeared only a few times, mostly associated with southwardly directed heliospheric magnetic field Bz. Using various methods such as self-organizing maps, statistical and superposed epoch analysis, we show that during and right after intense geomagnetic storms, there is growth in the number of transmission line failures. We also examine the temporal changes in the number of failures during 2010-2014 and find that the growing linear tendency of electrical grid failure occurrence is possibly connected with solar activity. We compare these results with the geoelectric field calculated for the region of Poland using a 1-D layered conductivity Earth model.

No abstract

Release of hazardous materials in chemical industries is a significant threat to surrounding areas. This thread can be answered by the reconstruction system capable of localizing the source of airborne contamination solely based on substance concentrations recorded by the sensors network. However, such systems require multiple runs of the selected atmospheric contaminant transport model. The complexity of the contaminated terrain involves the application of the complicated and computationally expensive dispersion models, while the fast one is too simplified. We examine the possibility of training an artificial neural network (ANN) so that it could effectively simulate the atmospheric toxin transport. The use of a fast neural network in place of costly computational dispersion models in systems localizing the source of contamination might significantly improve their efficiency (speed). In this paper, we train the ANN with the use of the training dataset covering the contamination source term parameters and point output concentrations generated by the Gaussian dispersion model. We test various ANN structures, i.e., numbers of ANN layers, neurons, and activation functions to achieve the ANN capable of estimating the contaminant concentration. Applying the specified ANN topology we train ANN with use of the real field Prairie Grass experiment data. The performed tests confirm that trained ANN has the potential to replace the dispersion model in the contaminant source localization systems.