![]() ![]() Based on this definition, the disturbance storm time index ( Dst) is established as the average of the disturbance variation of the H-component, divided by the average of the cosines of the dipole latitudes at the observatories for normalization to the dipole equator ( Sugiura & Kamei, 1991). Thus, a geomagnetic storm can be defined by ground-based low-latitude geomagnetic field horizontal component variations ( Gonzalez et al., 1994). This magnetic field orientation allows magnetic reconnection ( Akasofu, 1981) and energy transfer from the solar wind tbo the Earth’s magnetosphere causing a depression of the Earth’s magnetic field horizontal (H) component due to the diamagnetic effect generated by the azimuthal circulation of particles in the ring current ( Gonzalez et al., 1994 Echer et al., 2008). They can last from a few hours to several days ( Gonzalez et al., 1999). Geomagnetic storms are perturbations on the Earth’s magnetic field caused by the southward component of the interplanetary magnetic field (IMF). Palabras clave: Índice Dst Pronóstico Tormenta geomagnética Serie temporal Red neuronal artificial Algoritmo genético Los resultados muestran una buena aproximación entre las variaciones medidas y modeladas de Dst tanto en la fase principal como en la fase de recuperación de una tormenta geomagnética. Se encontró que el método propuesto ANN+GA puede ser adecuadamente entrenado para pronosticar Dst ( t+1 a t+6) con una precisión aceptable (con errores cuadrático medio RMSE≤10nT y coeficientes de correlación R≥0.9), y que los índices geomagnéticos utilizados tienen efectos influyentes en la buena capacidad de entrenamiento y predicción de la red elegida. Se analizaron diferentes topologías de ANN y se seleccionó la arquitectura óptima. La base de datos utilizada contiene 233,760 datos de índices geomagnéticos por hora desde 00 UT del 01 de enero de 1990 hasta las 23 UT del 31 de agosto de 2016. A partir de esta técnica, la ANN fue optimizada por GA para actualizar los pesos de la ANN y para pronosticar el índice Dst a corto plazo de 1 a 6 horas de antelación usando los valores de la serie temporal del índice Dst y del índice de electrojet auroral ( AE). Se desarrolló un método que combina una red neuronal artificial y un algoritmo genético (ANN+GA) con el fin de pronosticar el índice de tiempo de perturbación de tormenta ( Dst). #Omniweb nasa dst index seriesKey words: Dst index Forecast Geomagnetic storm Time series Artificial neural network Genetic algorithm The results show a good agreement between the measured and modeled Dst variations in both the main and recovery phases of a geomagnetic storm. It emerged that the proposed ANN+GA method can be properly trained for forecasting Dst ( t+1 to t+6) with good accuracy (with root mean square errors RMSE≤10nT and correlation coefficients R≥0.9), and that the utilized geomagnetic indices significantly affect the good training and predicting capabilities of the chosen network. Different topologies of ANN were analyzed and the optimum architecture was selected. The database used contains 233,760 hourly geomagnetic indices data from 00 UT on 01 January 1990 to 23 UT on 31 August 2016. #Omniweb nasa dst index updateThis technique involves optimizing the ANN by GA to update the ANN weights and to forecast the short-term Dst index from 1 to 6 hours in advance by using the time series values of the Dst and auroral electrojet ( AE) indices. A method that combines an artificial neural network and a genetic algorithm (ANN+GA) was developed in order to forecast the disturbance storm time ( Dst) index. ![]()
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