CNN-GRU water quality prediction method fusing attention mechanism

A technology of water quality prediction and attention, applied in the field of neural network and data mining, to reduce the complexity of the model and improve the accuracy

Pending Publication Date: 2020-12-22
中国科学院沈阳计算技术研究所有限公司
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Problems solved by technology

[0004] Aiming at the deficiencies in existing river water quality prediction methods, the technical problem mainly solved by the present invention is to use pollutant concentration, river water flow and water flow velocity as model input,

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  • CNN-GRU water quality prediction method fusing attention mechanism
  • CNN-GRU water quality prediction method fusing attention mechanism
  • CNN-GRU water quality prediction method fusing attention mechanism

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Embodiment Construction

[0049] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0050] The present invention proposes a CNN-GRU hybrid model that incorporates an attention mechanism. The CNN model has powerful feature extraction capabilities and can fully mine the information features in water quality data; the GRU model simplifies the gate structure of LSTM, making the model less complex and easier to converge; the attention mechanism can highlight the influence of important information, making The model is more optimized and the prediction accuracy is higher.

[0051] Such as figure 1 As shown, the CNN-GRU water quality prediction method incorporating the Attention mechanism includes the following steps:

[0052] Step 1: The automatic detection station is equipped with a pollutant concentration detection device and a hydrological information detection device. The pollutant concentration detection device includes an am...

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Abstract

The invention relates to a CNNGRU water quality prediction method fusing an attention mechanism. The method comprises the following steps of cleaning and normalizing pollutant concentration, river water flow rate and water flow rate data, constructing the data into a vector form, inputting the vector form into a CNN model for feature extraction, and taking feature vectors extracted by the CNN model as input of a GRU network for model training; meanwhile, an Attention mechanism being fused in the model training process to optimize model parameters, and finally, the trained model being used forshort-term prediction of river water quality. According to the method, the strong feature extraction capability of the CNN model, the simple and easy convergence of the GRU network structure and the outstanding important features of the Attention mechanism are fully fused, and accuracy of the model is improved; effectiveness of the method is verified through specific experiments.

Description

technical field [0001] The invention belongs to the technical field of neural network and data mining, and specifically relates to a CNN-GRU water quality prediction method integrated with an Attention mechanism. Background technique [0002] Rivers are an important part of life on earth, providing water for human life, production and agriculture, and are the basis for human survival and development. In recent years, the malicious discharge of a large amount of wastewater has caused more and more rivers to be seriously polluted, which has seriously threatened the ecological balance and sustainable economic development. River water quality prediction is an important means to protect river water environment and deal with river pollution. By predicting the concentration of pollutants in river water resources, it is possible to detect and deal with possible serious pollution events in time, and provide strong data support for the protection of river water resources. In recent ...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06Q10/04
CPCG06Q10/04G06N3/08G06N3/045
Inventor 王宁周晓磊胡衍坤刘枢姜秋俚张楠梁操王继娜
Owner 中国科学院沈阳计算技术研究所有限公司
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