JP / EN

Publications

Publications

研究業績・論文リスト

国際学術誌論文 (International Journal Papers)

  • Yıldırım, H. H., Ishida, K., Ercan, A., 2026. Spatiotemporal assessment of aridification in Europe (1950–2024) using bias-corrected high-resolution reanalysis dataset. Atmospheric Research 336, 108874. doi: 10.1016/j.atmosres.2026.108874
  • Hamada, N., Koga, H., Katsuya, K., Ishida, K., Ito, H., Kawagoshi, Y., 2024. Insight into groundwater quality change before and after the 2016 Kumamoto earthquake. Sci. Total Environ. 957, 177783.
  • Ishida, K., Ercan, A., Nagasato, T., Kiyama, M., Amagasaki, M., 2024. Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling. J. Environ. Manage. 359, 120931.
  • Ito H., Tsurumaki S., Hama T., Ishida K., Watanabe T., Duc L., and Kawagoshi Y., Aggregation and Dispersion Behaviours of Riverine Trace Metals (Fe, Al, V, Mn, Ni, and Zn) and Organic Matter in Freshwater and Estuarine Conditions: A case study in Shira and Midori Rivers, Kumamoto, Japan. Aquatic Geochemistry. 2022. (published online) doi: 10.1007/s10498-022-09408-7
  • Izumi, T., Amagasaki, M., Ishida, K., Kiyama, M., Super-resolution of sea surface temperature with convolutional neural network- and generative adversarial network-based methods. J. Water Clim. Chang., 13(4), pp.1673–1683, 2022. doi: 10.2166/wcc.2022.291
  • Sano T., Kawagoshi Y., Kokubo I., Ito H., Ishida K., and Sato A., Direct and indirect effects of membrane pore size on fouling development in a submerged membrane bioreactor with a symmetric chlorinated poly (vinyl chloride) flat-sheet membrane. Journal of Environmental Chemical Engineering, 10(2), 107023, 2022. doi: 10.1016/j.jece.2021.107023
  • Sano T., Ito H., Ishida K., Sato A., Duc LV., and Kawagoshi Y., Effect of Surface Hydrophilicity of Symmetric Polytetrafluoroethylene Flat-sheet Membranes on Membrane Fouling in a Submerged Membrane Bioreactor, Japanese Society of Water Treatment Biology, 57(4), pp.79-89, 2021. doi: 10.2521/jswtb.57.79
  • Iseri, Y., Diaz, A.J., Trinh, T., Levent Kavvas, M., Ishida, K., Anderson, M.L., Ohara, N., Snider, E.D., Dynamical downscaling of global reanalysis data for high-resolution spatial modeling of snow accumulation/melting at the central/southern Sierra Nevada watersheds. J. Hydrol. 126445, 2021. doi: 10.1016/j.jhydrol.2021.126445
  • Ishida, K., Kiyama, M., Ercan, A., Amagasaki, M., Tu, T., Multi-time-scale input approaches for hourly-scale rainfall–runoff modeling based on recurrent neural networks. Journal of Hydroinformatics 23, 1312–1324, 2021. doi: 10.2166/hydro.2021.095
  • Kimura, N., Ishida, K., Baba, D., Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach. Water 13, 1109, 2021. doi: 10.3390/w13081109
  • Tu, T., Ishida, K., Ercan, A., Kiyama, M., Amagasaki, M., Zhao, T., Hybrid precipitation downscaling over coastal watersheds in Japan using WRF and CNN. Journal of Hydrology: Regional Studies 37, 100921, 2021. doi: 10.1016/j.ejrh.2021.100921
  • Yokoo, K., Ishida, K., Ercan, A., Tu, T., Nagasato, T., Kiyama, M., Amagasaki, M., Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM. Sci. Total Environ. 802, 149876, 2021. doi: 10.1016/j.scitotenv.2021.149876
  • Tu, T., Ercan, A., Carr, K.J., Kavvas, M.L., Trinh, T., Ishida, K., Nosacka, J., Brown, K., Coupling hydroclimate-hydraulic-sedimentation models to estimate flood inundation and sediment transport during extreme flood events under a changing climate. Sci. Total Environ. 740, 140117, 2020. doi: 10.1016/j.scitotenv.2020.140117
  • Ishida, K., Tsujimoto, G., Ercan, A., Tu, T., Kiyama, M., Amagasaki, M., Hourly-scale coastal sea level modeling in a changing climate using long short-term memory neural network. Sci. Total Environ. 720, 137613, 2020. doi: 10.1016/j.scitotenv.2020.137613
  • Ishida, K., Tanaka, K. and Hama, T.: Sensitivity Analysis of Convective Parameterizations of a Regional Climate Model in Higher-Resolution Domains for Long-Term Precipitation Reconstruction. Journal of Water and Climate Change. 11(4), pp. 1467-1480, 2020. doi: 10.2166/wcc.2019.069
  • Hama, T., Fujimi, T., Shima, T., Ishida, K., Kawagoshi, Y. and Ito, H.: Evaluation of Groundwater Recharge by Rice and Crop Rotation Fields in Kumamoto, Japan. Journal of Water and Climate Change, 11(4), pp.1042-1049, 2019. doi: 10.2166/wcc.2019.045
  • Ishida, K., Ercan, A., Trinh, T., Jang, S., Kavvas, M. L., Ohara, N., Chen, Z. Q., Kure, S. and Dib, A.: Trend Analysis of Watershed-Scale Annual and Seasonal Precipitation in Northern California Based on Dynamically Downscaled Future Climate Projections. Journal of Water and Climate Change, 11(1), pp.86-105, 2020. doi: 10.2166/wcc.2018.241
  • Amin, I. M. Z. bin M., Ercan, A., Ishida, K., Kavvas, M. L., Chen, Z. Q. and Jang, S.-H.: Impacts of Climate Change on the Hydro-Climate of Peninsular Malaysia. Water, 11(9), p.1798, 2019. doi: 10.3390/w11091798
  • Gorguner, M., Kavvas, M. L. and Ishida, K.: Assessing the Impacts of Future Climate Change on the Hydroclimatology of the Gediz Basin in Turkey by Using Dynamically Downscaled CMIP5 Projections. Science of the Total Environment, 648, pp.481–499, 2019. doi: 10.1016/j.scitotenv.2018.08.167
  • Ishida, K., Ohara, N., Ercan, A., Jang, S., Trinh, T., Kavvas, M. L., Carr, K. and Anderson, M. L.: Impacts of Climate Change on Snow Accumulation and Melting Processes over Mountainous Regions in Northern California during the 21st Century. Science of The Total Environment. Elsevier, 685, pp.104–115, 2019. doi: 10.1016/j.scitotenv.2019.05.255
  • Mure-Ravaud, M., Ishida, K., Kavvas, M. L., Yegorova, E. and Kanney, J.: Numerical Reconstruction of the Intense Precipitation and Moisture Transport Fields for Six Tropical Cyclones Affecting the Eastern United States. Science of the Total Environment. Elsevier, 665, pp.1111–1124, 2019. doi: 10.1016/j.scitotenv.2019.02.185
  • Ishida, K., Ercan, A., Trinh, T., Kavvas, M. L., Ohara, N., Carr, K. and Anderson, M. L.: Analysis of Future Climate Change Impacts on Snow Distribution over Mountainous Watersheds in Northern California by Means of a Physically-Based Snow Distribution Model. Science of the Total Environment. Elsevier, 645, pp.1065–1082, 2018. doi: 10.1016/j.scitotenv.2018.07.250
  • Ishida, K, Kavvas, M. L., Chen, Z. Q. R., Dib, A., Diaz, A. J., Anderson, M. L. and Trinh, T.: Physically Based Maximum Precipitation Estimation under Future Climate Change Conditions. Hydrological Processes, 32(20), pp.3188–3201, 2018. doi: 10.1002/hyp.13253
  • Trinh T. *, M. L. Kavvas, K. Ishida, A. Ercan, Z.Q. Chen, M.L. Anderson, C. Ho, and T. Nguyen: Integrating global land-cover and soil datasets to update saturated hydraulic conductivity parameterization in hydrologic modeling. Science of The Total Environment, 632, pp. 279-288, 2018.
  • Ishida, K., Ohara, N., Kavvas, M. L., Chen, Z. Q. and Anderson, M. L.: Impact of Air Temperature on Physically-Based Maximum Precipitation Estimation through Change in Moisture Holding Capacity of Air. Journal of Hydrology. Elsevier, 556, pp.1050–1063, 2018. doi: 10.1016/j.jhydrol.2016.10.008
  • Torie, K., Cawthorne, D. L., Ishida, K., Kavvas, M. L. and Anderson, M. L.: Long-Term Trend Analysis on Total and Extreme Precipitation over Shasta Dam Watershed. Science of the Total Environment, 626, pp.244–254, 2018. doi: 10.1016/j.scitotenv.2018.01.004
  • Amin, M. Z. M., Shaaban, A. J., Ercan, A., Ishida, K., Kavvas, M. L., Chen, Z. Q. and Jang, S.: Future Climate Change Impact Assessment of Watershed Scale Hydrologic Processes in Peninsular Malaysia by a Regional Climate Model Coupled with a Physically-Based Hydrology Modelo. Science of The Total Environment, 575, pp.12–22, 2017. doi: 10.1016/j.scitotenv.2016.10.009
  • Ishida, K., Gorguner, M., Ercan, A., Trinh, T. and Kavvas, M. L.: Trend Analysis of Watershed-Scale Precipitation over Northern California by Means of Dynamically-Downscaled CMIP5 Future Climate Projections. Science of the Total Environment. Elsevier, 592, pp.12–24, 2017. doi: 10.1016/j.scitotenv.2017.03.086
  • Ishida, K. and Kavvas, M. L.: Climate Change Analysis on Historical Watershed-Scale Precipitation by Means of Long-Term Dynamical Downscaling. Hydrological Processes, 31(1), pp.35–50, 2017. doi: 10.1002/hyp.10932
  • Jang, S., Kavvas, M. L., Ishida, K., Trinh, T., Ohara, N., Kure, S., Chen, Z. Q., Anderson, M. L., Matanga, G. and Carr, K. J.: A Performance Evaluation of Dynamical Downscaling of Precipitation over Northern California. Sustainability (Switzerland). Multidisciplinary Digital Publishing Institute, 9(8), p.1457, 2017. doi: 10.3390/su9081457
  • Kavvas, M. L., Ishida, K., Trinh, T., Ercan, A., Darama, Y. and Carr, K. J.: Current Issues in and an Emerging Method for Flood Frequency Analysis under Changing Climate. Hydrological Research Letters, 11(1), pp.1–5, 2017. doi: 10.3178/hrl.11.1
  • Ohara, N., Kavvas, M. L., Anderson, M. L., Chen, Z. Q. and Ishida, K.: Characterization of Extreme Storm Events Using a Numerical Model-Based Precipitation Maximization Procedure in the Feather, Yuba, and American River Watersheds in California. Journal of Hydrometeorology, 18(5), 2017. doi: 10.1175/JHM-D-15-0232.1
  • Trinh, T., Ishida, K., Kavvas, M. L., Ercan, A. and Carr, K.: Assessment of 21st Century Drought Conditions at Shasta Dam Based on Dynamically Projected Water Supply Conditions by a Regional Climate Model Coupled with a Physically-Based Hydrology Model. Science of The Total Environment. Reston, VA: American Society of Civil Engineers, 586, pp.197–205, 2017. doi: 10.1016/j.scitotenv.2017.01.202

国内学術誌論文 (Domestic Journal Papers)

  • 永里 赳義, 石田 桂, 坂口 大珠: Out-of-Sample LSTM による高解像度積雪深分布推定, AIデータサイエンス論文集, Vol3, No.J2, p889-897, 2022.
  • 坂口 大珠, 石田 桂, 永里 赳義: リサンプリングとアンサンブル学習を用いた深層学習降雨流出モデルの精度向上の試み, AIデータサイエンス論文集, Vol3, No.J2, p906-945, 2022.
  • 永里 赳義, 石田 桂, 横尾 和樹, 坂口大珠, 木山 真人, 尼崎 太樹: 深層学習を用いた欠測流量データの補完方法の提案, 土木学会論文集B1(水工学) Vol.77, No.2, I_1243-I_1248, 2021.
  • 横尾 和樹, 石田 桂, 永里 赳義, 坂口 大珠, 木山 真人, 尼崎 太樹: LSTMを用いた降雨流出解析における入力変数の物理的意味と精度への影響に対する考察, AIデータサイエンス論文集, Vol2, No.J2, p883-892, 2021.
  • 坂口 大珠, 石田 桂, 横尾 和樹, 永里 赳義, 木山 真人, 尼崎 太樹: アンサンブル学習による河川流量推定における弱学習器に対する考察, AIデータサイエンス論文集, Vol2, No.J2, p872-882, 2021.
  • 永里 赳義, 石田 桂, 上田 誠, 横尾 和樹, 木山 真人, 尼崎 太樹: 深層学習を用いた降水量ダウンスケーリングの特性, 土木学会論文集B1(水工学) Vol.76, No.2, I_373-I_378, 2020.

国際学会議事録 (International Conference Proceedings)

  • Nagasato, T., Ishida, K., Yokoo, K., Kiyama, M., Amagasaki, M., 2021. Effects of the spatial and temporal resolution of meteorological data on the accuracy of precipitation estimation by means of CNN, in: IOP Conference Series: Earth and Environmental Science. Presented at the International Conference on Geological Engineering and Geosciences (ICGoES), Virtual Meeting, Mar 2021, IOP Publishing, p. 012033.
  • Yokoo, K., Ishida, K., Nagasato, T., Ercan, A., Tu, T., 2021a. Comparison of three recurrent neural networks for rainfall-runoff modelling at a snow-dominated watershed, in: IOP Conference Series: Earth and Environmental Science. Presented at the International Conference on Geological Engineering and Geosciences (ICGoES), Virtual Meeting, Mar 2021, IOP Publishing, p. 012012.
  • Yokoo, K., Ishida, K., Nagasato, T., Kawagoshi, Y., Ito, H., 2021b. Reconstruction of groundwater level at Kumamoto, Japan by means of deep learning to evaluate its increase by the 2016 earthquake, in: IOP Conference Series: Earth and Environmental Science. Presented at the International Conference on Geological Engineering and Geosciences (ICGoES), Virtual Meeting, Mar 2021, IOP Publishing, p. 012032.

学会口頭発表 (Oral Presentations)

  • Satori TERUYA, Kei ISHIDA, Akira SATO: Soil Classification and Characterization Using K-Means Clustering of the National Geotechnical Information Data, The 20th International Student Conference on Advanced Science and Technology (ICAST) 2025, Chiayi, Taiwan, Nov 2025
  • Satori TERUYA, Kei ISHIDA, Akira SATO: Evaluation of the Accuracy of Geological Classifications in Borehole Data Using Clustering, Taiwan-Japan 2025 Joint Conference on Civil Engineering, Kaohsiung, Taiwan, Aug 2025.
  • 浦越 淳・石田 桂・永里 赳義・照屋 智里：CNNを用いた欠測データ補完による河川流量予報の精度向上, 令和6年度土木学会西部支部研究発表会, 沖縄県中頭郡西原町，Mar. 2025.
  • 七田 隼成・石田 桂・浦越 淳・照屋 智理：深層学習を用いた河川流量予報における学習データ範囲外への適用可能性，令和6年度土木学会西部支部研究発表会, 沖縄県中頭郡西原町，Mar. 2025.
  • 永田 光一・石田 桂・浦越 淳・照屋 智理：将来気候予測における不確実性が予測結果にあたえる影響の定量的評価，令和6年度土木学会西部支部研究発表会, 沖縄県中頭郡西原町，Mar. 2025.
  • 照屋 智理・石田 桂・佐藤 晃・浦越 淳：国土地盤情報データの土質試験データを用いた変数間の関係性解析，令和6年度土木学会西部支部研究発表会, 沖縄県中頭郡西原町，Mar. 2025.
  • 照屋 智理・石田 桂・佐藤 晃・浦越 淳：日本全国の土質データを用いた深層学習による地盤の透水係数の推定, 2024年度資源・素材関係学協会合同秋季大会，秋田県秋田市，Sep.2024.
  • 井手 佑成，石田 桂: 河川流量予報に適した深層学習モデルの構築, 令和5年度土木学会西部支部研究発表会, Mar. 2024.
  • 浦越 淳，石田 桂，永里 赳義，上田 誠: 深層学習を用いた河川流量予報における積雪深データの影響評価, 令和5年度土木学会西部支部研究発表会, Mar. 2024.
  • 照屋 智理，石田 桂，浦越 淳: 日本全国の土質データを用いた地盤の透水係数の推定, 令和5年度土木学会西部支部研究発表会, Mar. 2024.
  • Sunao URAGOE, Kei ISHIDA, Makoto UEDA and Takeyoshi NAGASATO: Evaluation of the impact of input variables on river flow forecasting using deep learning, 2023 Engineering Workshop, Kumamoto, Japan, Dec 2023.
  • Sunao URAGOE, Kei ISHIDA, Makoto UEDA and Takeyoshi NAGASATO: Improving Accuracy of River Flow Forecasting Using Deep Learning by Utilizing Various Data as Input Variables, Korea-Japan 2023 Joint Conference on Civil Engineering, Busan, Korea, Aug 2023.
  • 大内田 華音，石田 桂，永里 赳義，坂口 大珠: Transformerを用いた降水量予報, 令和4年度土木学会西部支部研究発表会, Mar. 2023.
  • 浦越 淳，石田 桂，永里 赳義，坂口 大珠: Transformerを用いた洪水予報の改善, 令和4年度土木学会西部支部研究発表会, Mar. 2023.
  • 坂口 大珠，石田 桂，永里 赳義: 深層学習を用いた地下水位モデリング, 令和4年度土木学会西部支部研究発表会, Mar. 2023.
  • 濱口 勇太，石田 桂: 熊本県内を対象とした水稲生育モデルのパラメータセッティング, 令和4年度土木学会西部支部研究発表会, Mar. 2023.
  • Takeyoshi Nagasatoto, Kei Ishida and Daiju Sakaguchi: Detailed Analysis of Stream Flow Complementation by Means of Deep Learning, International Student Conference on Advanced Science and Technology (ICAST), 12-16, Dec. 2022, Kumamoto, Japan.
  • Daiju Sakaguchi, Kei Ishida and Takeyoshi Nagasatoto: Accuracy comparison of rainfall-runoff modelingusing ensemble learning for CNN, International Student Conference on Advanced Science and Technology (ICAST), 12-2, Dec. 2022, Kumamoto, Japan.
  • Yuta Hamaguchi, and Kei Ishida: Parameter setting of Crop model at Kumamoto region by literature survey, International Student Conference on Advanced Science and Technology (ICAST), 12-15, Dec. 2022, Kumamoto, Japan.
  • 永里 赳義，石田 桂，横尾 和樹，坂口 大珠: LSTMを用いた欠測流量補完手法の開発, 令和3年度土木学会西部支部研究発表会, Mar. 2022.
  • 玉置 慎，石田 桂，横尾 和樹，永里 赳義，坂口 大珠: 深層学習手法を用いた積雪深推定, 令和3年度土木学会西部支部研究発表会, Mar. 2022.
  • 横尾 和樹，石田 桂，永里 赳義，坂口 大珠: 複数の深層学習手法を用いた降雨流出モデリングの高精度化, 令和3年度土木学会西部支部研究発表会, Mar. 2022.
  • 坂口 大珠，石田 桂，横尾 和樹，永里 赳義: アンサンブル学習スタッキングを用いた河川流量推定におけるXGBoostのハイパーパラメータの影響分析, 令和3年度土木学会西部支部研究発表会, Mar. 2022.
  • 北村 俊輔，石田 桂，竹内 裕希子，永里 義赳，坂口 大珠: 数値解析を用いた洪水氾濫形態と避難経路の安全性に関する考察, 令和3年度土木学会西部支部研究発表会, Mar. 2022.
  • 井ノ口 宗佑，石田 桂，横尾 和樹，永里 赳義，坂口 大珠: 降水量モデリングにおけるCNNの特性, 令和3年度土木学会西部支部研究発表会, Mar. 2022.
  • 濱口 勇太，石田 桂: 1993年の冷夏による熊本県の稲への影響, 令和3年度土木学会西部支部研究発表会, Mar. 2022.
  • Kazuki Yokoo, Takeyoshi Nagasatoto Daiju Sakaguchi, and Kei Ishida: Improvement of Accuracy of Rainfall-Runoff modeling with LSTM by Means of Integrated Sensitivity Analysis International Student Conference on Advanced Science and Technology (ICAST), 12-2, Dec. 2021, Kumamoto, Japan.
  • Shin Tamaoki, Kazuki Yokoo Takeyoshi Nagasatoto, and Kei Ishida: The effect of setting hyperparameters on the accuracy of snow depth modeling by utilizing LSTM by Means of Integrated Sensitivity Analysis International Student Conference on Advanced Science and Technology (ICAST), 12-2, Dec. 2021, Kumamoto, Japan.
  • Sosuke Inokuchi, Kei Ishida, Kazuki Yokoo, and Takeyoshi Nagasatoto: The effect of CNN structure on estimation accuracy of precipitation downscaling, International Student Conference on Advanced Science and Technology (ICAST), 12-2, Dec. 2021, Kumamoto, Japan.
  • Daiju Sakaguchi, Kei Ishida, Kazuki Yokoo, and Takeyoshi Nagasatoto: Comparison of Random Forest and Linear Regression in a weak learner for river flow estimation by using ensemble learning, International Student Conference on Advanced Science and Technology (ICAST), 12-2, Dec. 2021, Kumamoto, Japan.
  • Takeyoshi Nagasatoto, Kei Ishida, and Daiju Sakaguchi: Sensitivity analysis of input variables in precipitation downscaling using 3D-CNN, International Student Conference on Advanced Science and Technology (ICAST), 12-2, Dec. 2021, Kumamoto, Japan.
  • Kazuki Yokoo, Kei Ishida, Takeyoshi Nagasato, Masato Kiyama, and Motoki Amagasaki: Investigation of Learning Process of Deep Learning Method for Rainfall-Runoff Modeling, ASCE 2021 World Environmental & Water Resources Congress, Online Meeting, June, 2021.
  • Takeyoshi Nagasato, Kei Ishida, Kazuki Yokoo, Masato Kiyama, and Motoki Amagasaki: Effects of Input Variables Selection on Accuracy of Watershed-scale Precipitation Downscaling by Means of Convolutional Neural Network, ASCE 2021 World Environmental & Water Resources Congress, Online Meeting, June, 2021.
  • Kei Ishida, Takeyoshi Nagasato, and Kazuki Yokoo: Severe Storm at Kuma River Watershed in Japan in July, 2020, ASCE 2021 World Environmental & Water Resources Congress, Online Meeting, June, 2021.
  • 永里 赳義, 石田 桂, 横尾 和樹:降水量ダウンスケーリングにおけるCNNの時間・空間方向への拡張, 令和2年度土木学会西部支部研究発表会, Mar. 2021.
  • 横尾和樹, 石田 桂, 永里 赳義: LSTM を用いた降雨流出モデリングにおける学習過程の考察, 令和2年度土木学会西部支部研究発表会, Mar. 2021.
  • 塩足蒼天,石田 桂, 横尾 和樹, 永里 赳義: 深層学習を用いた降水量ダウンスケーリングにおける入力変数の物理的特性, 令和2年度土木学会西部支部研究発表会, Mar. 2021.
  • Takeyoshi Nagasatoto, Kei Ishida and Kazuki Yokoo: Flood flow estimation by means of deep learning methods International Student Conference on Advanced Science and Technology (ICAST), 10-1, Dec. 2020, Kumamoto, Japan.
  • Kazuki Yokoo, Takeyoshi Nagasatoto and Kei Ishida: A Study on Learning Process of Deep Learning Method Using Rainfall-Runoff Modeling International Student Conference on Advanced Science and Technology (ICAST), 10-1, Dec. 2020, Kumamoto, Japan.

学会ポスター発表 (Poster Presentations)

  • 照屋智理，石田桂，佐藤晃：国土地盤情報データを用いた物性値の関係性及び透水係数推定式の精度解析，令和７年度土木学会全国大会 in Kumamoto, Sep.2025.
  • 照屋智理，石田桂，佐藤晃：経験則的物性値を用いた土質試験データとボーリングデータ間の分類精度検証，第2回九州圏水工学研究会，Sep.2025.
  • Junsei Shichida, Kei Ishida: Evaluating the extrapolation capability of deep learning in rainfall-runoff, EGU General Assembly 2025, Vienna, Austria, 27 April–2 May 2025, EGU25-14457.
  • Koichi Nagata, Kei Ishida: Quantitative analysis of the impact of realization selection on future climate change impact assessments using CMIP6 data, EGU General Assembly 2025, Vienna, Austria, 27 April–2 May 2025, EGU25-9648.
  • Satori Teruya, Kei Ishida, Akira Sato: Analysis of relationships among variables in nationwide big data of geotechnical information in Japan, EGU General Assembly 2025, Vienna, Austria, 27 April–2 May 2025, EGU25-12841.
  • 照屋智理，石田桂，佐藤晃：国土地盤情報データを用いた土質試験データと透水係数の関係解析，第１回九州圏水工学研究会，Sep.2024.
  • Nagasato, T., Ishida, K., Sakaguchi, D., Amagasaki, M., and Kiyama, M.: Sensitivity analysis of network structure in missing streamflow data complementation using Bidirectional Short-Term Memory network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10803.
  • Sakaguchi, D., Ishida, K., Nagasato, T., Amagasaki, M., and Kiyama, M.: Improvement of river flow estimation accuracy using ensemble learning stacking, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10891.
  • Takeyoshi Nagasato, Kei Ishida, and Daiju Sakaguchi: Effect of optimization method selection in precipitation downscaling using convolutional neural network, Frontiers in Hydrology Meeting, Online Meeting, June. 2022.
  • Daiju Sakaguchi, Kei Ishida, and Takeyoshi Nagasato: Comparison of deep learning methods in river flow estimation, Frontiers in Hydrology Meeting, 2022 AGU Fall Meeting, Online Meeting, June. 2022.
  • Takeyoshi Nagasato, Kei Ishida, and Daiju Sakaguchi: Effect of Hidden State Selection in Output Layer in Streamflow Complementation Using Bidirectional Long Short-Term Memory Network, 2022 AGU Fall Meeting, Online Meeting, Dec. 2022.
  • Takeyoshi Nagasato, Kei Ishida, and Daiju Sakaguchi: Effectiveness of oversampling in precipitation downscaling using deep learning, PAWEES International Conference 2022, Fukuoka, Nov. 2022.
  • Daiju Sakaguchi, Kei Ishida, and Takeyoshi Nagasato: Higher accuracy of groundwater modeling using deep learning methods, PAWEES International Conference 2022, Fukuoka, Nov. 2022.
  • Yuta Hamaguchi, and Kei Ishida: Evaluation of the effect of cold damage in 1993 on paddy rice yield in Kumamoto Prefecture, Japan, PAWEES International Conference 2022, Fukuoka, Nov. 2022.
  • Kazuki Yokoo, Kei Ishida, Takeyoshi Nagasato, Masato Kiyama, and Motoki Amagasaki: Sensitivity Analysis of Hyper Parameters of Long Short-Term Memory Networks for Rainfall-Runoff Modeling at Snow-Dominated Watersheds, ASCE 2021 World Environmental & Water Resources Congress, Online Meeting, June, 2021.
  • Takeyoshi Nagasato, Kei Ishida, and Kazuki Yokoo: Reconstruction of Severe Flood at Kuma River Basin during 2020 July Storm by Means of Deep Learning Method, ASCE 2021 World Environmental & Water Resources Congress, Online Meeting, June, 2021.
  • Kei Ishida, Kazuki Yokoo, and Takeyoshi Nagasato: Effects of Topography on Intense Precipitation during the 2017 July Northern Kyushu Storm in Japan, ASCE 2021 World Environmental & Water Resources Congress, Online Meeting, June, 2021.
  • Takeyoshi Nagasato, Kei Ishida, Kazuki Yokoo, Masato Kiyama and Motoki Amagasaki: Sensitivity Analysis of the Hyperparameters of CNN for Precipitation Downscaling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4400.
  • Kazuki Yokoo, Kei Ishida, Takeyoshi Nagasato, and Ali Ercan: Effect of input variables on rainfall-runoff modeling using a deep learning method, EGU 2021 European Geosciences Union, Online Meeting, April 2021.
  • Takeyoshi Nagasato, Kei Ishida, Kazuki Yokoo, Masato Kiyama and Motoki Amagasaki: Comparison between 2D-CNN a 3D-CNN for precipitation downscaling, 2020 AGU Fall Meeting, Online Meeting, Dec. 2020.
  • Kazuki Yokoo, Kei Ishida, Takeyoshi Nagasato, Masato Kiyama and Motoki Amagasaki: Behavior of International Valiables Long and Short-Term Memory Neural Network for Rainfall-Runoff Modeling, 2020 AGU Fall Meeting, Online Meeting, Dec. 2020.
  • Kazuki Yokoo, Kei Ishida, Takeyoshi Nagasato, Daiju Sakaguchi, Ali Ercan, Masato Kiyama and Motoki Amagasaki: Applicability of precipitation data from reanalysis as input to rainfall-runoff model using LSTM, 2021 AGU Fall Meeting, Online Meeting, Dec. 2021.
  • Takeyoshi Nagasato, Kei Ishida, Kazuki Yokoo, Daiju sakaguchi, Masato Kiyama, and Motoki Amagasaki: Complement method of missing streamow time-series data by means of Long and Short-TermMemory network, 2021 AGU Fall Meeting, Online Meeting, Dec. 2021.
  • Daiju Sakaguchi, Kei Ishida, Kazuki Yokoo, Takeyoshi Nagasato, Yasunori Kawagoshi, and Hiroaki Ito: Improving the accuracy of Kumamoto's groundwater level modeling using deep learning LSTM, 2021 AGU Fall Meeting, Online Meeting, Dec. 2021.