1. 1) Okazaki, T., Morikawa, N., Iwaki, A., Fujiwara, H., Iwata, T. and Ueda, N.: Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records, Bulletin of the Seismological Society of America, Vol. 111, No. 4, pp. 1740-1753, 2021. https://doi.org/10.1785/0120200339
2. 2) Kubo, H., Kunugi, T., Suzuki, W., Suzuki S. and Aoi, S.: Hybrid Predictor for Ground-Motion Intensity with Machine Learning and Conventional Ground Motion Prediction Equation, Scientific Reports, Vol. 10, No. 11871, 2020. https://doi.org/10.1038/s41598-020-68630-x
3. 3) Matsuoka, K. and Ohno, S.: Study on Ground Motion Spectrum Evaluation Using Machine Learning, Proceedings of the Annual Meeting of Japan Association for Earthquake Engineering, B-4-5, 2020 (in Japanese, title translated by the authors).
4. 4) Oana, A., Ishii, T., Miyashita, Y. and Furukawa, K.: Trial of Construction of Ground Motion Evaluation Models by Machine Learning: Study of Features on Volcanic Fronts and Rupture Directivity Effects, Proceedings of the Annual Meeting of Japan Association for Earthquake Engineering, T2021-010, 2021 (in Japanese, title translated by the authors).
5. 5) Ishii, T., Oana, A., Miyashita, Y. and Furukawa, K.: Trial Study on the Possibility of Machine Learning Considering the Features Based on the Ground Motion Prediction Equation, Proceedings of the Annual Meeting of Japan Association for Earthquake Engineering, T2021-012, 2021 (in Japanese, title translated by the authors).