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

点击翻译按钮获取中文摘要

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