River water quality prediction method based on feature engineering

Abstract

The invention discloses a river water quality prediction method based on feature engineering. The method comprises the following steps: step A, acquiring monitoring data influencing river water quality; step B, preprocessing the monitoring data influencing the water quality of the river; c, constructing characteristic indexes influencing the water quality of the river; step D, performing feature index screening by adopting a correlation coefficient method, including performing feature screening of the LSTM model through a Pearson correlation coefficient method; e, model structure data processing: carrying out LSTM model structure data preprocessing on the screened characteristic indexes; f, performing model training and automatic parameter adjustment, including training the LSTM model and automatically adjusting and optimizing the parameters of the LSTM model, and storing the optimal model to a server; and G, model prediction and result evaluation: obtaining prediction data based on the optimal model, and evaluating the deviation between the prediction data and real data. In combination with multiple factors and multiple condition influence factors of the river, the prediction condition of the water quality change is reflected more accurately and more comprehensively.

Description

technical field [0001] The invention relates to the technical field of water quality prediction, in particular to a method for predicting river water quality based on feature engineering. Background technique [0002] With the acceleration of urbanization, the pollution of rivers in cities is becoming more and more serious. In order to more efficiently supervise the water quality of rivers and the discharge of pollutants entering rivers, and improve the efficiency of prevention and control of water pollution in rivers, environmental management departments need to be more timely, accurate, and Understand the changes of river water quality more comprehensively, and take measures in advance for the main discharge outlets to improve the quality ratio of river water. Therefore, it is very important to predict the water quality of river gushes. [0003] At present, water quality prediction methods mainly include: traditional prediction methods such as regression analysis predicti...

AI Technical Summary

Problems solved by technology

On the one hand, rainfall will affect external source pollution such as upstream water and surface runoff; on the other hand, rainfall will affect the physical self-purification process of water bodies

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