LSTM-RNN model-based air pollutant concentration forecast method

Abstract

The invention relates to an LSTM (Long Short-Term Memory)-RNN model-based air pollutant concentration forecast method. The method comprises the steps of monitoring and collecting air pollutant concentration data in a relatively long time; preprocessing historical data to construct training, verification and test sample data of a to-be-trained LSTM-RNN model; obtaining a pre-trained LSTM-RNN model through the training sample data, then performing fine adjustment and training through the constructed verification and test sample data to obtain LSTM-RNN model parameters, improving model precision by further correcting model parameters, and taking a corrected LSTM-RNN model as an air pollutant concentration forecast model; and finally taking the preprocessed air pollutant concentration data of a target city in the relatively long time as input data of the LSTM-RNN model, and performing model output to obtain a forecast result of air pollutant concentration at a current moment or at a moment in the future.

Description

technical field [0001] The invention relates to an air pollutant concentration forecasting method based on an LSTM-RNN model, belonging to the field of environmental pollution forecasting. Background technique [0002] With the rapid development of industrialization and urbanization, the scale of energy and transportation has gradually expanded, and the urban population has expanded rapidly. Air pollution has increasingly become a regional problem, and the nature of pollution has gradually changed to compound pollution. Environmental pollution needs to be solved urgently. The concentration of air pollutants, such as carbon monoxide, nitrogen oxides, hydrocarbons, sulfur oxides and particulate matter, has adverse effects on human health or ecosystems. Therefore, it is necessary to forecast the current or future air pollutants based on the previous air pollutant concentration data, so as to adjust or limit real-time industrial emissions, traffic flow and other conditions to e...

AI Technical Summary

Problems solved by technology

[0011] The technology of the present invention solves the problem: overcomes the deficiencies of the prior art, and proposes a method for forecasting the concentration of air pollutants based on the LSTM-RNN model, which makes up for the deficiencies of the prior art, has strong generalization ability, and achieves higher prediction accuracy

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