Water quality prediction method based on gated circulation unit network integration

Abstract

A high-precision prediction algorithm is designed for the current situation that water quality changes are difficult to predict to cause pollution accidents. According to the algorithm, an integratedgating circulation unit is innovatively used for processing time series data of historical water quality indexes, so that water quality prediction and analysis are carried out. The algorithm is builtby a Keras tool of PYTHON3.6. 5, and comprises the steps of integrating five gating cycle unit networks, and integrating the five gating cycle units by using an averaging method. The time series dataof the multiple water quality indexes are used as the input of the algorithm, and finally the high-accuracy water quality effect is obtained. The algorithm provides a new solution for water quality prediction, can provide reliable evaluation and early warning for water pollution prevention and control, and is further widely applied to the field of water quality prediction.

Description

【Technical field】 [0001] The invention relates to a water quality prediction method based on gated circulation unit network integration, which belongs to the field of water environment detection. 【Background technique】 [0002] Water is the source of life and a necessary condition for the survival of organisms and economic development. At present, nearly half of the 666 cities in my country are short of water. To make matters worse, in the case of such a shortage of water resources, due to the rapid development of my country's industrialization process and the decline in the self-purification capacity of rivers, more than 70% of the river sections of the country's seven major water systems have been polluted, and about 90% of urban waters have been polluted. heavily polluted. The problem of water pollution has become one of the most important restrictive factors for my country's economic and social development, and the prevention and control of water pollution has been high...

AI Technical Summary

Problems solved by technology

[0003] Due to the diversity and complexity of water quality components, there is often a highly nonlinear relationship between water quality indicators and the traditional linear method is difficult to establish an accurate water quality prediction model

At present, the research on typical water quality simulation models mainly uses MIKE21 hydraulic water quality model, EFDC hydrodynamic module, AnnAGNPS watershed hydrological non-point source model and a combination of one-dimensional random water quality degradation and two-dimensional migration and diffusion models, but the establishment of these models needs to be fully considered The impact on the natural environment requires professional personnel to evaluate, and a large amount of prior knowledge needs to be used in the establishment of the model

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