BiGRU multi-step prediction method and system applied to flood prediction and storage medium

Abstract

The invention belongs to the technical field of information processing, and discloses a BiGRU multi-step prediction method and system applied to flood prediction and a storage medium. In an Attentionmechanism layer, a hidden layer state sequence vector is input into a learnable function to generate a probability vector, and a subsequent hidden layer intermediate vector is generated by vector weighted averaging; the Attention circularly calculates the adaptive weighted average of the hidden layer state sequence of each time step to generate the intermediate vector; the important information ofeach time step is output backwards according to a certain weight; and the information integration capability is realized as time goes on. According to the BiGRU multi-step prediction method, the information can be stored in the memory as time goes on, and the BiGRU multi-step prediction method has great advantages in processing time sequence problems; and in combination with an Attention mechanism, a test result shows that the BiGRU multi-step flood forecasting model based on the Attention mechanism can better forecast the flood peak arrival time and the flood peak value.

Description

technical field [0001] The invention belongs to the technical field of information processing, and in particular relates to a BiGRU multi-step prediction method, system and storage medium applied to flood prediction. Background technique [0002] At present, all over the world, floods caused by extreme rainstorms cause a large number of casualties and economic losses every year. Flood forecasting is a very important measure in the process of flood control and disaster reduction. The accuracy of flood forecast will directly affect the implementation of measures such as reservoir dispatching, flood control and emergency rescue, and industrial and agricultural safety. Currently, there are still many ununderstood questions about the behavior of precipitation and the underlying physical laws. It is not easy to recognize all the complex relationships between various aspects of a dynamic process. One of the challenges of flood forecasting is the selection of models. In the past ...

AI Technical Summary

Problems solved by technology

However, due to the complexity of the flood formation process, it is often difficult for traditional flood forecasting models to make accurate and reliable forecast results when predicting long-term hydrological flow series

[0005] Through the above analysis, the problems and defects of the existing technology are: due to the complexity of the flood formation process, it is often difficult for the traditional flood forecasting model to make accurate and reliable forecast results when predicting long-term hydrological flow series

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