Water quality index multi-step prediction method integrating wavelet decomposition and a deep neural network

Abstract

The invention discloses a water quality index multi-step prediction method integrating wavelet decomposition and a deep neural network. The method comprises the following steps: 1) filling missing values of monitoring historical data of original water quality indexes by using a linear interpolation method; (2) carrying out discrete wavelet transform on the data after the missing values are filled up; 3) constructing an encoder based on the bidirectional gating circulation unit, constructing a decoder based on the unidirectional gating circulation unit and the full connection layer, extracting the correlation between the hidden layer state of each step of the decoder and the states of all the hidden layers of the encoder in combination with an attention mechanism, constructing a neural network model in combination with the encoder and the decoder, and taking a plurality of decomposition sequences obtained after wavelet decomposition as input. The method has the advantages that the characteristics of nonlinearity and complex fluctuation of water quality data are fully considered, the noise influence is weakened, the influence degree of historical data in each step is adaptively extracted, the water quality indexes of multiple weeks in the future are predicted in a one-step and end-to-end mode, and the method has practical application value for water resource management and ecological guarantee.

Description

technical field [0001] The invention belongs to the field of deep learning, and in particular relates to a multi-step water quality index prediction method integrating wavelet decomposition and deep neural network. Background technique [0002] Water is the source of life and a necessary guarantee for human production and life. However, with the development of science and technology, the water consumption of industrial activities has increased rapidly; with the increase of life expectancy and population expansion, domestic water consumption has also increased day by day. At present, researchers at home and abroad have been carrying out related research on water quality prediction, and have proposed many different methods. At present, the main methods used are water quality simulation model, regression analysis, time series analysis, machine learning and so on. Among the current water quality prediction methods, the water quality simulation model method has a small scope of ...

AI Technical Summary

Problems solved by technology

In general, the multi-step prediction of water quality data still lacks systematic discussion and optimization.

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