Exploration of Flood Prediction in Watersheds Based on the Fusion Analysis of Remote Sensing Big Data with Multiple Physical Fields - Minming Xu; Yun Ouyang (Journal of Function Spaces)
It is well known that flooding brings great losses to people’s production and life, just because it is unsuspected, is extremely extensive, and has high frequency. More than half of the people in China live in flood-prone areas, and their lives and properties are threatened. Flood risk assessment is one of the measures of flood management, and it is economically and socially important to assess flood risk. The scope of the traditional monitoring and forecasting early warning system is mainly limited to the area affected by flash floods, while the objective reality of real-time monitoring, forecasting, and early warning of flood disasters in the area affected by river floods and by the scheduling of riverine terrace power stations is not possible. The multiphysics remote sensing big data fusion analysis can divide the data into grids according to the grid method in the sliding time window, filter the normal data by information entropy in each grid, judge the remaining data that may be abnormal by using local abnormality factor, and eliminate the abnormal data according to the judgment result. This paper introduces the application of remote sensing in flood control field, proposes the framework of basin flood prediction based on multiphysics field remote sensing big data fusion analysis, and designs each functional module of the system according to the object-oriented idea to realize the functions of data management, image processing, spatial analysis, and simulation output. The method can change the problems of cumbersome data processing and basic parameter rate determination in traditional hydrological methods and can find certain regularity through the connection between all related factors. Meanwhile, the use of artificial intelligence and other technical means makes the calculation speed faster and the obtained results are closer to the actual measured values, which is beneficial to guide the practical work.