An online prediction method of water quality parameter nitrite nitrogen based on dbn-bp

Abstract

The invention discloses an online prediction method of water quality parameter nitrite nitrogen based on DBN‑BP, comprising the following steps: (1) Water quality data collection: the online water quality detection system continuously collects water quality parameter data and corresponding water quality parameter data in the water body Under the nitrite nitrogen concentration data, get the original data; the water quality parameters include temperature value, pH value, dissolved oxygen value, oxidation-reduction potential value; (2) data preprocessing: the collected original data are processed by filtering abnormal data sequentially , rough set reduction processing, and data normalization processing to obtain the test sample data set; (3) Calculate the predicted value: input the test sample data set into the DBN‑BP model for training, and calculate the predicted value. The prediction model has good performance and excellent prediction effect, and can be used as an online prediction method for water quality parameter nitrite nitrogen to improve the accuracy of nitrite nitrogen determination.

Description

technical field [0001] The invention relates to the field of water quality detection, in particular to an online prediction method of water quality parameter nitrite nitrogen based on DBN-BP. Background technique [0002] With the development of society, the amount of available water resources is decreasing year by year; at the same time, the inefficiency of water resource utilization and waste are particularly common, and the more serious problem is that the pollution of water resources has not been effectively resolved. At the same time as the rapid economic development, the population surges, and the pollutants brought inevitably enter the water body, especially lakes, causing eutrophication of the water body, stimulating the growth of blue-green algae, and forming algal blooms. Cyanobacteria blooms often contain cyanotoxins, which are extremely harmful to aquatic animals and humans. After the death of cyanobacteria, they decompose, and a large amount of organic matter i...

AI Technical Summary

Problems solved by technology

[0002] With the development of society, the amount of available water resources is decreasing year by year; at the same time, the inefficiency of water resource utilization and waste are particularly common, and the more serious problem is that the pollution of water resources has not been effectively resolved

However, the current prediction methods for nitrite nitrogen content prediction at home and abroad are all shallow-structured machine learning methods, because the basic theory based on it lacks robustness in processing big data, resulting in a general lack of long-term effectiveness and scalability of the model

In addition, changes in nitrite nitrogen content in aquaculture water are affected by biological metabolism, water temperature, air pressure, air temperature, relative humidity, solar radiation, wind speed, wind direction, chemical oxygen demand (COD), biological oxygen demand (BOD), plant photosynthesis and artificial Operational activities and many other factors are cross-influenced, and the mechanism of action is complex, which makes the data of nitrite nitrogen content have strong non-stationarity, large time lag and nonlinear characteristics, mixed with a large number of noise signals. Nitrogen content data is highly connected, and direct modeling and prediction will seriously affect the accuracy of model prediction

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