Water quality prediction method and system based on multi-source data fusion and deep learning

Abstract

The invention provides a water quality prediction method and system based on multi-source data fusion and deep learning. The prediction method comprises the following steps: determining a drainage basin range and internal partitions thereof; determining and preprocessing basic meteorological parameters of the drainage basin in the prediction time period; determining sub-basin parameters of the sub-basin in the prediction time period, generating a sub-basin parameter set, and then preprocessing the sub-basin parameter set to generate original data; constructing a full-connection deep learning neural network, and defining a loss function and an iterative optimization algorithm; obtaining a trained deep learning neural network; and inputting the test data set of the original data into the trained deep learning neural network, and predicting to obtain corresponding water quality parameters. Compared with the prior art, the method and the system have the advantages that data does not need to be subjected to time sequence arrangement, multiple types of data can be fused, water quality change prediction can be performed more conveniently, and prediction efficiency and practicability are improved; and the invention is simple and easy to operate and can be easily applied to actual water quality management.

Description

Technical field [0001] The present invention relates to the field of water quality prediction technology, particularly, to a method and system for quality prediction based on multiple learning data fusion and depth. Background technique [0002] Surface water quality forecasting is an important support environmental governance rivers and lakes of water. In general, water quality monitoring sites by the water quality of rivers and lakes regularly measured to obtain water quality data as a basis for the water environment governance. However, the limited number of water quality monitoring sites, high construction and maintenance costs, it is difficult to achieve coverage of large spatial extent. Thus, by a mathematical model to predict changes in water quality become economically viable way. [0003] Water is predicted in a certain spatial range, the control unit identifying the time variation characteristic or quality index and quality of water in response to environmental factors ...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem of the singleness of water quality prediction in the background technology, the prediction accuracy of prediction methods and models is not high, and the failure to use multiple factors to predict water quality, the present invention adopts a water quality prediction based on multi-source data fusion and deep learning method and system

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