A Neural Network Prediction Method of Air Quality Based on Decision Tree Index
An air quality and neural network technology, applied in the field of data processing, can solve the problems of low air quality inflection point recognition ability and reporting rate, not taking advantage of various statistical algorithms, and unable to provide public health guidance, etc. and forecasting ability, improving applicability and timeliness, and the effect of simple forecasting steps
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[0080] See Figure 3 to Figure 6 , the present invention is a kind of method based on the neural network prediction air quality of decision tree index, comprises the following steps:
[0081] (1) Establish a time series data set of relevant meteorological factors, air quality and air pollutant emissions;
[0082] (2) Use the decision tree DT algorithm to classify the acquired training samples, and generate the optimal tree structure T oriented by air quality characteristics α and their corresponding classification results;
[0083] (3) according to the classification result, set up a BP neural network model for each classification, and carry out model training;
[0084] (4) Input prediction data set, carry out classification index based on decision tree, select trained DT-BP neural network model or comprehensive BP neural network to predict air quality;
[0085] (5) Obtain continuous air quality forecast results based on iterative algorithms;
[0086] (6) Record the number...
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