Runoff probability prediction method and system based on deep learning

Abstract

The invention belongs to the technical field of runoff prediction, and discloses a runoff probability prediction method and system based on deep learning, and the method comprises the steps: employinga maximum information coefficient to analyze the linear and nonlinear correlation between variables, so as to screen a runoff correlation factor; building an extreme gradient boosting tree model on the basis of correlation analysis, and inputting runoff correlation factors into a trained XGB model to complete runoff point prediction; inputting a point prediction result obtained by the XGB model into a GPR model, and performing secondary prediction to obtain a runoff probability prediction result; selecting confidence and acquiring a runoff interval prediction result under the corresponding confidence through Gaussian distribution; and optimizing hyper-parameters in the XGB model and the GPR model by adopting a Bayesian optimization algorithm. A high-precision runoff point prediction result, an appropriate runoff prediction interval and reliable runoff probability prediction distribution can be obtained, and the prediction method plays a crucial role in utilization of water resourcesand reservoir scheduling.

Description

technical field [0001] The invention belongs to the technical field of runoff prediction, and in particular relates to a runoff probability prediction method and system based on deep learning. Background technique [0002] Currently, the closest prior art: [0003] Hydropower energy is clean, cheap and renewable green energy. The biggest influencing factor of reservoir operation is runoff. Therefore, realizing high-precision and reliable runoff probability forecast is of great significance for reservoir dispatching to achieve comprehensive benefits such as flood control, power generation, water supply and shipping. However, the formation process of rainfall runoff is affected by many natural factors such as hydrology, topography, and meteorology, and presents highly nonlinear, random, and uncertain characteristics, which makes the accuracy of traditional machine learning methods for predicting runoff limited. In recent years, deep learning methods have been widely used in ...

AI Technical Summary

Problems solved by technology

[0006] (1) The basic data collected by the process-driven runoff prediction method is too complex, and its model solution is very time-consuming

[0007] (2) In the data-driven runoff prediction method, the traditional machine learning method has limited prediction accuracy due to the complex characteristics of runoff

[0008] (3) Previous runoff forecasting models are mostly deterministic forecasting models, and it is difficult to quantify the uncertainty of forecasting

[0011] (2) Deep learning methods are widely used in the fields of speech recognition and image processing, but they are rarely used in the field of runoff prediction, and the reference literature is limited

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