GA-optimized neural network for forecasting the geomagnetic storm index

Vega-Jorquera, Pedro; Lazzús, Juan A.; Rojas, Pedro Díaz

doi:10.22201/igeof.00167169p.2018.57.4.2104

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…In this situation the Lund operational forecast becomes unreliable, while EDDA still produces valid predictions. Vörös et al (2002) have made the interesting suggestion of using the information about the scaling characteristics of magnetic fluctuations as an additional input to a neural network. They have implemented this by computing the so-called Hölder exponent of past geomagnetic time series and shown that it significantly improved the prediction accuracy.…”

Section: Forecasting D Stmentioning

confidence: 99%

“…Yet a different method to train a neural network, based on a Genetic Algorithm (GA) has been presented in Vega-Jorquera, Lazzús, and Rojas (2018), where one to six hours ahead predictions were developed using a single hidden layer NN. The results were very good for one hour ahead, but degraded strongly for 6 hours ahead (RM SE ∼ 14).…”

Section: Forecasting D Stmentioning

confidence: 99%

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The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

2019

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The numerous recent breakthroughs in machine learning make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in Space Weather. The purpose is twofold. On one hand, we will discuss previous works that use machine learning for Space Weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the Space Weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray box. Plain Language SummaryIn the last decade, machine learning has achieved unforeseen results in industrial applications. In particular, the combination of massive data sets and computing with specialized processors (graphics processing units, or GPUs) can perform as well or better than humans in tasks like image classification and game playing. Space weather is a discipline that lives between academia and industry, given the relevant physical effects on satellites and power grids in a variety of applications, and the field therefore stands to benefit from the advances made in industrial applications. Today, machine learning poses both a challenge and an opportunity for the space weather community. The challenge is that the current data science revolution has not been fully embraced, possibly because space physicists remain skeptical of the gains achievable with machine learning. If the community can master the relevant technical skills, they should be able to appreciate what is possible within a few years time and what is possible within a decade. The clearest opportunity lies in creating space weather forecasting models that can respond in real time and that are built on both physics predictions and on observed data.

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Section: Forecasting D Stmentioning

confidence: 99%

Section: Forecasting D Stmentioning

confidence: 99%

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

2019

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Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms

et al. 2019

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In this study, different hybridized techniques that combine an artificial neural network (ANN) with bio‐inspired optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), and a hybridized PSO+GA were applied to update the ANN connection weights and so forecast the disturbance storm time (Dst) index. The past values of Dst index time series were used as input parameters to forecast its variation from 1 to 6 hours ahead. The database collected considers 233,760 hourly data from 01 January 1990 to 31 August 2016, containing storms and quiet period, grouped into three data sets: learning set (116,880 hourly data points), validation set (58,440 data points), and testing set (58,440 data points). Several ANN configurations were studied and optimized during the training process by evaluating the root mean square error (RMSE) and the correlation coefficient (R). An analysis of the predictive capability of each method was made year per year, and according to the levels of the geomagnetic storm. Also, an additional test was applied to the proposed method using 17 intense geomagnetic storms reported during solar cycle 24, including the St. Patrick's Day storm of 2015. Results show that the hybridized ANN+PSO method can forecast the Dst index quite accurately from 1 to 3 h in advance (with RMSE≤5 nT and R≥0.9), while the ANN+PSO+GA method can forecast the Dst index quite accurately from 4 to 6 h ahead (RMSE≤7 nT and R≥0.8)

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