Nowadays, geomagnetic storm forecasting is one of the main subjects of the space weather studies. The space weather and the solar-terrestrial relationship have been under investigation for a long time. In addition, the results show a strong dependence on the solar wind electric current. The results are outstanding in term of accuracy when considering a medium-term prediction of 12 hr in advance and in term of timing of the Dst minimum occurrence. Generally, the duration and number of the input parameters significantly affect the training and prediction performance of the applied ANN. The results indicate an adequate accuracy of R = 0.876 for prediction 2 hr in advance and R = 0.857 for prediction 12 hr in advance. Several ANN structures were tested and the best results were determined using the correlation coefficient ( R) during the training and prediction phases. The input parameters are the solar wind interplanetary magnetic field, southward component of interplanetary magnetic field, temperature, density, speed, pressure, and electric field. The ANN uses 24 past hourly solar wind parameters values to forecast the Dst index. We propose an artificial neural network for the prediction of disturbance storm time ( Dst) index 1 to 12 hr ahead. The results are outstanding in term of accuracy when considering a medium-term prediction of 12 hr in advance and in terms of timing of the Dst minimum occurrence. The power of the proposed ANN was illustrated using the strongest six storms recorded during the prediction period. While the period from 1 January 2016 to was used to test the prediction capabilities of the ANN. The ANN was trained on the data period from 1 January 2007 to 31 December 2015, which contains 78,888 hourly data samples. The input parameters are the solar wind interplanetary magnetic field, north-south component of interplanetary magnetic field, temperature, density, speed, pressure, and electric field. The ANN uses past near-Earth solar wind parameter values to forecast the Dst. To view data from the Dst Index, please visit the WDC Kyoto Observatory.In this work, we propose an artificial neural network (ANN) with seven input parameters for the prediction of disturbance storm time ( Dst) index 1 to 12 hr ahead. Students interested in learning more about geomagnetism may be interested in a set of Sugiura described Dst derivation in ANNALS OF THE IGY. Latitude transforms residual variations to their equatorial equivalents and harmonic analysis Hourly H-component magnetic variations are analyzed to remove annual secular change trendsįrom records of a worldwide array of low-latitude observatories. Kamei, WDC-C2 for Geomagnetism, Faculty of Science, Kyoto University, Kyoto 606, Japan. This diskette contains the hourly indices for the period through, as derived by M. They show the effect of the globally symmetrical westward flowing high altitude equatorial ring current, which causes the "main phase" depression worldwide in the H-component field during large magnetic storms. Dst is maintained at NGDC and is available viaĭst (Disturbance Storm Time) equivalent equatorial magnetic disturbance indices are derived from hourly scalings of low-latitude horizontal magnetic variation. Geomagnetic observatories that measures the intensity of the globally symmetrical equatorialĮlectrojet (the "ring current"). The Dst index is an index of magnetic activity derived from a network of near-equatorial
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