Decision tree index-based neural network air quality prediction method

Abstract

The invention relates to a decision tree index-based neural network air quality prediction method. The method comprises the following steps of establishing a time sequence data set of related meteorological factors, air quality and atmospheric pollutant discharge; classifying the obtained training samples by using a decision tree DT algorithm to generate an optimal tree structure T alpha orientedby air quality characteristics and a corresponding classification result; according to the classification result, establishing a BP neural network model for each classification, and performing model training; inputting a prediction data set, performing classification indexing based on a decision tree, and selecting the trained DT-BP neural network model or the comprehensive BP neural network to predict the air quality; obtaining a continuous air quality prediction result based on an iterative algorithm; recording the frequency of occurrence of data sets which do not meet the decision tree classification matching rule, and automatically starting model updating when a set value is exceeded. The method is suitable for predicting and forecasting the air quality of conventional weather, abruptchange weather and heavy pollution weather.

Description

technical field [0001] The invention belongs to the technical field of data processing, and relates to a method for forecasting air quality suitable for conventional weather, sudden change weather and heavily polluted weather, in particular to a method for predicting air quality based on a neural network based on a decision tree index. Background technique [0002] With the rapid growth of our country's economy and the continuous development of urbanization, the problem of environmental pollution has increasingly seriously affected the space on which people live, and even caused major vicious accidents, which greatly endangered people's health and production and construction. For a long time, researchers have conducted comprehensive and systematic research on the change characteristics and trend forecast of regional ambient air quality. However, since air pollution is affected by various factors such as weather background, topography, transportation and convergence, and the ...

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Problems solved by technology

[0020] (2) When using the trained model for forecasting, it is necessary to use the data of the last L days to do an error test on the forecasting model of each decision tree, and then determine which tree's forecasting data to choose. The steps are relatively complicated

[0021] To sum up, the existing air quality forecasting methods and systems all

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