Short-term water quality and quantity prediction method and system based on deep learning

Abstract

The invention provides a short-term water quality and water quantity prediction method based on deep learning, and the method comprises the following steps: A, preprocessing original water quality andwater quantity data, and dividing the processed data into a training set and a test set; B, inputting the training set into an LSTM network for training, and updating the weight by using an adam algorithm to obtain a prediction model; C, predicting a prediction value in the test set by using a prediction model based on the original water quality and water quantity data; D, inputting the prediction error into the ARMA model to obtain an error correction model of an error sequence; E, inputting to-be-predicted data into the prediction model and the error correction model, and geometrically adding settlement results to obtain a predicted value; the invention further provides a water quality and quantity prediction system. The invention has the advantages that the LSTM neural network and theARMA model are used for calculating the water quality and water quantity at the moment to be predicted and the prediction error respectively, higher universality and stability are achieved, and the water quality and water quantity prediction result is more stable.

Description

technical field [0001] The invention relates to the technical field of water environment protection and monitoring; in particular, it relates to a short-term water quality and water quantity prediction method and system based on deep learning. Background technique [0002] Water is one of the most important resources in human society. At present, the Yellow River, Songhua River, and Huaihe River basins in my country are all slightly polluted, while some areas in the Haihe River and Liaohe River basins are in a state of severe pollution. Timely prediction of water quality and quantity can be known in advance The possibility of water pollution, as well as the abnormality of water flow, will help to detect water environment problems in the area in time, and provide an important basis for the management and maintenance of the water environment in local water sources. It is also a research in the field of water environment protection and monitoring in recent years. One of the hot s...

AI Technical Summary

Problems solved by technology

[0004] The change of river water quality and quantity has the characteristics of gradual change, nonlinearity, instability, and complexity. Currently, the neural network models used for water quality and quantity prediction, such as BP neural network, radial basis function neural network, and generalized neural network, perform complex time It is easy to fall into local optimum when sequence prediction

At the same time, due to the strong nonlinear characteristics and weak linear characteristics of the water quality and quantity data series, it is difficult for a single prediction model to fully deal with the weak linear characteristics of water quality and quantity, and it needs to be combined with other linear algorithms

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