In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.

1. INTRODUCTION
2. DATA AND METHODS
    2.1. Data
    2.2. MLR and MLP
    2.3. Statistical Verification
3. RESULTS AND DISCUSSION
4. SUMMARY AND CONCLUSION
REFERENCES

• JONGYEOP PARK(School of Space Research, Kyung Hee University)
• YONG-JAE MOON(Department of Astronomy and Space Science, Kyung Hee University, School of Space Research, Kyung Hee University)
• JIN-YI LEE(Department of Astronomy and Space Science, Kyung Hee University) CORRESPONDING AUTHOR
• EUI-JUN JEONG(Department of Astronomy and Space Science, Kyung Hee University)