Water quality fluctuating range prediction method based on combination of deep learning algorithm and mixed integer linear programming

Abstract

The invention discloses a water quality fluctuating range prediction method based on a combination of a deep learning algorithm and mixed integer linear programming, and belongs to the fields of water environmental protection and monitoring study. The method comprises the steps of firstly, introducing an LSTM (Long Short Term Memory) model architecture suitable for time series data prediction in the deep learning algorithm into modeling of water quality time series point prediction; and secondly, considering that the LSTM model has strong time series prediction performance but has common problems of deterministic prediction methods, namely cannot be used for estimating the indeterminacy of prediction, constructing a general range prediction model based on mixed integer linear programming according to the deviation of validation set sample LSTM point prediction and real values and a confidence parameter, thus giving a water quality fluctuating range prediction method under certain confidence. The method can provide a new solution for water quality prediction, and then provides reliable evaluation and early warning basis for water quality early warning.

Description

technical field [0001] The invention relates to a water quality fluctuation interval prediction method based on the combination of deep learning algorithm and mixed integer linear programming, which belongs to the research field of water environment protection and monitoring. Background technique [0002] Water is the source of life, and human beings cannot do without water in life and production activities. With the rapid development of my country's economy, the expansion of population, and the decline of the self-purification capacity of rivers and lakes, rivers and lakes in my country are generally polluted to varying degrees, and 75% of the lakes in the country have experienced eutrophication to varying degrees. Water pollution reduces the use function of water bodies, exacerbates the shortage of water resources, and has a negative impact on the implementation of my country's sustainable development strategy. Based on the historical data of water quality monitoring, a w...

AI Technical Summary

Problems solved by technology

[0003] The water environment system is a complex system affected by many factors such as biology, chemistry, physics, and man-made, and the water quality changes nonlinearly with time, so it is difficult to establish an accurate water quality prediction model with traditional methods

Images