Development of flood control supporting system based on Severe Rainfall prediction system using Artificial Intelligence (SRAI)

基于人工智能强降雨预报系统（SRAI）的防洪支持系统开发

• 批准号: 11555134
• 负责人: IKEBUCHI Shuichi
• 金额: $ 8.51万
• 依托单位: KYOTO UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 1999
• 资助国家: 日本
• 起止时间: 1999 至 2001
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-11555134/
• 关键词: short term rainfall forecast; integrated flood control; qualitative reasoning; MRI; NPD NHM; severe rainfall; river management; disaster prevention; cloud microphysical processes; Truth Maintenance System; PC-UNIX; サブシステム; メタシステム; 人工知能; 協調問題

The purpose of this research is in making the flood control support system, and the application of this to an actual flood. The accuracy improvement of Severe Rainfall prediction system using Artificial Intellige nee (SRAI) which is the kernel of the system was done became indispensable for that. SRAI is the method to qual itatively forecast the breaking out and development of the rainy cloud of small area and short term, by using the radar, the GMS image, and the numerical prediction data as an initial value.It was difficult for GPV to achieve the forecast accuracy improvement time because it used the data from RSM (Regional Spectral Model) as an initial value. Then, the forecast value of the NHM (Meteorological Research Institute/ Numerical Prediction Division Non Hydrostatic Model), which was the next generation numeric meteorological model (5km space resolution every hour) was introduced, and the improvement of the forecast accuracy was aimed at. The accuracy of rainfall forecast o … More f two-three hours has been improved.In addition, because the improvement of the rainfall forecast accuracy was able to be expected, the mixing ratio of "cloud water" and "rain water" which NHM forecast was made to be used as an initial value of SRAI in the process of the research on the accuracy improvement, has been used. Therefore, SRAI in the present stage is a system which can forecast the rainfall for a short time by taking, and processing all information on NHM. However, because a lot of time had been spared to development, the application case was not able to be increased. It will be assumed to be a problem to improve reliability to the user by increasing the application case in the future.It has pride that making an application for the forecast by a numeric meteorological model to the management site in the river and the reservoir by overcoming the problem of NHM (The rainfall of 100 % cannot be forecasted, necessary of highly meteorological knowledge is necessary for the interpretation, and a large amount of data) by using NHM at the end is a big result in this research. Less

本研究的目的是建立防汛支持系统，并将其应用于实际洪涝灾害。而作为系统核心的人工智能预报系统(SRAI)，其预报精度的提高势在必行。SRAI是利用雷达、GMS云图和数值预报资料作为初值对小范围、短时雨云的爆发和发展进行定量预报的方法，由于GPV采用区域谱模式RSM(Region Spectral Model)资料作为初值，难以达到预报精度的提高时间。然后，介绍了下一代数值气象模式--NHM(气象研究所/数值预报部门非静力模式)的预报值(空间分辨率为每小时5公里)，并对提高预报精度进行了探讨。…对降雨预报的准确性此外，由于降水预报精度的提高是可以预期的，因此在提高精度的研究过程中，采用了NHM预报的“云水”和“雨水”的混合比作为SRAI的初始值。因此，现阶段的SRAI是一个通过获取和处理NHM上的所有信息来进行短期降水预报的系统。然而，由于大量的时间被腾出用于开发，应用案例无法增加。通过增加应用实例来提高用户的可靠性是一个值得骄傲的问题。通过克服NHM(100%的降雨量不能预报，需要高度的气象知识才能进行解释，数据量大)的问题，将数值气象模式的预报应用到河流和水库的管理点，是本研究的一大成果。较少

项目成果

大石哲, 舛田直樹, 池淵周一: "定性的短時間強雨予測手法による洪水制御支援手法の開発"土木学会論文集. 691/II-57. 13-23 (2001)

Satoshi Oishi、Naoki Masuda、Shuichi Ikebuchi：“利用定性短期强降雨预测方法开发防洪支持方法”，日本土木工程师学会会刊 691/II-57 (2001)。

大石 哲: "GPS大気遅延量のパワースペクトルを用いた小規模水蒸気擾乱の変動特性解析"土木学会水工学論文集. 第44巻. 11-12 (2000)

Satoshi Oishi：“利用 GPS 大气延迟的功率谱分析小尺度水汽扰动的波动特征”，日本土木工程学会水利工程学报，第 44 卷，11-12（2000 年）。

堀智晴: "地域防災計画に基づく災害対応シミュレーションモデル"京都大学防災研究所年報. 42B-2. 311-318 (1999)

Tomoharu Hori：“基于区域防灾计划的灾害响应模拟模型”，京都大学防灾研究所年度报告42B-318（1999）。

堀智晴: "地域防災計画に基づく災害対応シミュレーションモデル"京都大学防災研究所年俸. 42B-2. 311-318 (1999)

Tomoharu Hori：“基于区域防灾计划的灾害响应模拟模型”，京都大学防灾研究所年薪42B-318（1999）。

大石哲, 舛田直樹, 池淵周一: "定性的短時問強雨予測手法による洪水制御支援手法の開発"土木学会論文集. 691/II-57. 13-23 (2001)

Satoshi Oishi、Naoki Masuda、Shuichi Ikebuchi：“使用定性短期暴雨预测方法开发防洪支持方法”日本土木工程师学会会议记录 691/II-57 (2001)。