Publications

  1. Kaneko, R., S. Onomura, and M. Nakayoshi. Deep Learning Approach for Rainfall Forecasting Using U-Net with Data Augmentation. in JpGU Meeting 2021. 2021.
  2. Monda, H., R. Kaneko, S. Onomura, and M. Nakayoshi, Attempt of One-Month Weather Forecast Using Machine Learning. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 2020. 65: p. Ⅰ_331-336.
  3. Kaneko, R., S. Onomura, and M. Nakayoshi, Investigation of a Real-Time Rain Forecasting Using U-Net. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 2020. 65: p. Ⅰ_403-408.
  4. Kaneko, R. and M. Nakayoshi, Examination of Rainfall Predictability by Machine-Learning Lstm Model Trained with Amedas Data. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 2020. 76(1): p. 129-139.
  5. Ito, T., R. Kaneko, T. Kataoka, S. Onomura, and Y. Nihei. Deep Learning Approach for Prediction of Water Level in Rivers. in 22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division: Creating Resilience to Water-Related Challenges, IAHR-APD 2020. 2020.
  6. 金子凌 and 仲吉信人, Lstm を用いた降水予測の検討. IEICE Technical Report, 2019. 119(202): p. 25-29.
  7. 伊藤毅彦, 金子凌, 片岡智哉, 小野村史穂, and 二瓶泰雄, リカレントニューラルネットワークを用いた鬼怒川の洪水時水位予測. IEICE Technical Report, 2019. 119(202): p. 43-44.
  8. Nihei, Y., A. Shinohara, K. Ohta, S. Maeno, R. Akoh, Y. Akamatsu, T. Komuro, T. Kataoka, S. Onomura, and R. Kaneko, Flooding Along Oda River Due to the Western Japan Heavy Rain in 2018. Journal of Disaster Research, 2019. 14(6): p. 874-885.
  9. Kaneko, R., S. Onomura, and M. Nakayoshi. Rainfall Prediction by a Recurrent Neural Network Algorithm Lstm Learning Surface Observation Data. in AGU Fall Meeting Abstracts. 2019.
  10. Kaneko, R. and M. Nakayoshi, Study of New Rainfall Prediction Method Based on Deep Learning. Advances in River Engineering, 2019. 25: p. 115-120.
  11. Ito, T., J. Kashiwada, N. Harayama, R. Kaneko, T. Kataoka, S. Onomura, M. Nakayoshi, and Y. Nihei, Nowcast and Forecast Modeling of Water-Level Profiles in Rivers Based on Improved Diex-Flood and Deep Learning. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 2019. 75(2): p. I_217-I_222.
  12. Kondo, S., M. Nakayoshi, and R. Kaneko, Investigation of Mechanism on the Highest Air Temperature in Japan 41.0 Degree Celsius Observed at Ekawasaki. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 2018. 74(5): p. I_1183-I_1188.
  13. Kondo, S., M. Nakayoshi, A. Ito, R. Kaneko, Y. Takane, and H. Kusaka. Investigation of Mechanism on the Highest Air Temperature in Japan 41.0 ºc Observed at Ekawasaki. in 10th International Conference on Urban Climate/14th Symposium on the Urban Environment. 2018. AMS.
  14. Kaneko, R. and M. Nakayoshi, Numerical Analysis of Heavy Rainfall in Northern Kyushu Region in July 2017. Advances in River Engineering, 2018. 24: p. 421-426.
  15. Kaneko, R. and M. Nakayoshi. Investigation of Effect of Building Morphology on Sea Breeze Advancement Based on Meso-Scale Simulation. in 10th International Conference on Urban Climate/14th Symposium on the Urban Environment. 2018. AMS.
  16. Kaneko, R. and M. Nakayoshi, Effect of Building Morphology on Seabreeze Advancement. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 2017. 73(4): p. I_445-I_450.ss