Feature extraction hydrological forecasting method based on deep learning

Abstract

The invention provides a feature extraction hydrological forecasting method based on deep learning, and belongs to the field of water resource efficient utilization and hydrological forecasting. The method comprises the following steps: firstly, obtaining a watershed hydrological forecasting characteristic factor set by utilizing watershed historical information; secondly, training the characteristic factor set by utilizing a data mining algorithm, and obtaining a plurality of groups of session flood process sets with similar magnitudes and process forms under the action of different factors;then, carrying out parameter calibration of all models and methods in traditional hydrological forecasting based on a deep learning algorithm, forming a model library and a method library matched withthe models, the methods and parameter schemes, and finally completing hydrological forecasting calculation in combination with clustering analysis. Compared with an existing method, the method has the advantages that the defects that a traditional hydrological forecasting method is low in forecasting precision, short in effective forecasting period and the like are effectively overcome, the forecasting precision can be obviously improved and the forecasting period can be obviously prolonged when hydrological forecasting is carried out, good applicability and feasibility are achieved, and an effective technical method is provided for basin hydrological forecasting.

Description

technical field [0001] The invention relates to the fields of efficient utilization of water resources and hydrological forecasting, and more specifically, a method for feature extraction and hydrological forecasting based on deep learning. Background technique [0002] Hydrological forecasting is not only an important technical support for flood and drought disaster prevention, but also an important means for reservoir scheduling and efficient resource utilization. There are many models and methods related to hydrological forecasting, most of which can reflect some basic laws of hydrology. However, due to the limited understanding of hydrometeorological phenomena in river basins and the intricate changes in natural laws, traditional models and methods are difficult to fully reflect objective laws, such as statistical forecasting. The method usually faces the problem of insufficient consideration of the physical meaning, while the land-atmosphere coupling method often has th...

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Problems solved by technology

[0006] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art and provide a method for hydrological forecasting based on deep learning feature extra

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