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Reservoir water level model prediction method

A reservoir water level and model forecasting technology, applied in forecasting, nuclear methods, calculation models, etc., can solve problems such as difficult water level changes, waterlogging, economic losses, etc., and achieve the effect of ensuring real-time forecasting

Pending Publication Date: 2021-04-30
中水智联(深圳)技术有限公司
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Problems solved by technology

Such a real-time monitoring method can effectively monitor the reservoir, but there are also shortcomings, mainly reflected in the time lag. The two methods of equipment monitoring and human monitoring are passive monitoring, which can only be observed after the water level changes, and for the future. It is difficult to make a scientific judgment on the change of water level
In fact, when a dangerous situation occurs, the water level changes extremely rapidly, and failure to predict in advance will lead to a hasty response to the dangerous situation
For example, although the meteorological department has predicted in advance that after July 3 this year, there will be continuous heavy rainfall in the areas along the middle and lower reaches of the Yangtze River, the Jianghuai River, and the southwestern and eastern regions. As of July 6, the 277 reservoirs in Wuhan City There are still 190 floods beyond the flood limit water level, the whole city is severely flooded, transportation, power supply, etc. are basically paralyzed, which has brought very significant negative impacts and also caused very huge economic losses

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  • Reservoir water level model prediction method

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Embodiment 1

[0029] A preferred embodiment of the present invention provides a reservoir water level model prediction method, comprising the following steps:

[0030] Step 1: collect the information data of the selected reservoir;

[0031] Step 2: Select time t, and obtain multiple water levels at different locations at time t according to the regression of the collected information;

[0032] Step 3: Take the average value of multiple water levels at time t;

[0033] Step 4: Predict the water level after a certain period of time through the obtained average value and variation of the water level.

[0034] The rise and fall of the water level of the reservoir is the result of the increase and decrease of water in the reservoir. The reduction of reservoir water includes evaporation, industrial water supply, domestic water supply, agricultural water supply, reservoir outflow, etc. The increase in reservoir water volume includes rainfall and inflow. Ideally, if we could measure all of thes...

Embodiment 2

[0046] The difference with embodiment 1 is: adopt random forest algorithm;

[0047] Including the following steps:

[0048] Use N to represent the number of training cases, and M to represent the number of features; the number of input features m is used to determine the decision result of a node on the decision tree, where m is much smaller than M; from the N training cases with replacement sampling way, sample N times to form a training set, and use unsampled use cases to make predictions and evaluate their errors; for each node, m features are randomly selected, and the decision of each node on the decision tree is determined based on these features ;According to the m features, calculate the best splitting method; each tree will grow completely and will not be pruned, and will only be pruned after a normal tree classifier is built.

[0049] Below this embodiment proves by an experiment:

[0050] Based on the water level data and rainfall data of four reservoirs in Shenzh...

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Abstract

The invention relates to the technical field of reservoir monitoring, in particular to a reservoir water level model prediction method which comprises the following steps: step 1, collecting information data of a selected reservoir; 2, a moment t is selected, and multiple water levels at different positions at the moment t are obtained through regression according to the collected information amount; 3, taking an average value of a plurality of water levels at the moment t; 4, predicting the water level quantity after a certain period of time according to the obtained water level average value and variation; according to the method, advanced practical technologies such as machine learning, compressed sensing, a neural network and deep learning are used, meteorological and hydrological big data are used for estimating the water level of the reservoir in real time, a theoretical method of big data science is applied to the hydrological field, real-time prediction of the water level of the reservoir is guaranteed, and prevention can be conducted in advance.

Description

technical field [0001] The invention relates to the technical field of reservoir monitoring, in particular to a reservoir water level model prediction method. Background technique [0002] At present, my country mainly adopts two methods of sensor equipment monitoring and human monitoring to monitor the water level of the reservoir to ensure real-time control of the water level information of the reservoir. Such a real-time monitoring method can effectively monitor the reservoir, but there are also shortcomings, mainly reflected in the time lag. The two methods of equipment monitoring and human monitoring are passive monitoring, which can only be observed after the water level changes, and for the future. It is difficult to make a scientific judgment on the change of water level. In fact, when a dangerous situation occurs, the water level changes extremely rapidly, and failure to predict in advance will lead to a hasty response to the dangerous situation. For example, alth...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/08G06N7/00G06N20/10
CPCG06Q10/04G06Q50/06G06N3/08G06N20/10G06N7/01
Inventor 曾庆彬苏腾飞邱鹏程陈正石俊豪王玉花
Owner 中水智联(深圳)技术有限公司
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