Flash flood early warning method based on deep learning

A flash flood warning and deep learning technology, applied in neural learning methods, nuclear methods, alarms, etc., can solve problems such as difficult operation, huge amount of calculation, and short forecast time.

A flash flood warning and deep learning technology, applied in neural learning methods, nuclear methods, alarms, etc., can solve problems such as difficult operation, huge amount of calculation, and short forecast time.

CN110459036AActive Publication Date: 2019-11-15HYDRAULIC SCI RES INST OF SICHUAN PROVINCE
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  • Flash flood early warning method based on deep learning
  • Flash flood early warning method based on deep learning
  • Flash flood early warning method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0093] Embodiment: a kind of flash flood warning method based on deep learning comprises the following steps:

[0094]The first step: determine the early warning object: outline the watershed where the early warning area is located on the GIS map, determine the mountain torrent early warning area in the watershed and its preset early warning rainfall or water level, and the deployed rainfall and water level monitoring stations;

[0095] The second step: collect and compile data: collect historical data of rainfall, water level stations and regional evaporation monitoring stations in the early warning area, and county meteorological early warning, and calculate the confluence time of the basin;

[0096] Among them, the calculation method of the confluence time of the watershed is the calculation of the inference formula or the calculation of the Thiessen polygon method or the combination of the above two.

[0097] The calculation method of the reasoning formula is: select the t...

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Abstract

The invention discloses a flash flood early warning method based on deep learning. According to the method, an RNN (recurrent neural network) model with an LTSM (long short-term memory) function is adopted. Problems of gradient disappearance and gradient explosion in the RNN can be effectively solved by adding the LTSM to a model algorithm, prediction of small watersheds or flash flood areas can be performed more effectively, reliable flash flood disaster early warning and predictionare achieved, and prediction time is greatly shortened. The model is only required to focus on input data, so that the model has the characteristics of being convenient to operate and high in operation speed, and facilitating value shifting to other areas. Moreover, with the practical application of the model,data is accumulated continuously, no secondary parameter adjustment is required, parameters can be optimized and adjusted actively by the model, and accordingly, the model is simpler and more convenient to use.

Description

technical field [0001] The invention relates to the field of disaster early warning, in particular to a deep learning-based mountain torrent early warning method. Background technique [0002] The dynamic early warning of mountain torrents is realized by issuing early warnings to areas in danger of torrents of torrents. As of 2018, my country has delineated flash flood danger areas nationwide, and Sichuan Province has more than 28,000 flash flood danger areas. These flash flood warnings are currently carried out by setting rainfall or water level warning indicators. The specific method is to set up rainfall or water level stations in the area, link the flash flood danger area (can be multiple) to the corresponding measuring station, and give early warning through the actual measured rainfall (water level). In order to prolong the warning time and improve the warning accuracy, some areas have begun to use hydrological models for flash flood warning, but this warning method ...

Claims

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Application Information

Patent Timeline
15 Nov 2019
Publication
CN110459036A
IPC
G08B21/10; G06N3/04; G06N3/08; G06N20/10
CPC
G08B21/10; G06N20/10; G06N3/084; G06N3/045; Y02A50/00
Inventors
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