Work

Air Quality Prediction Model Based on BP Neural Network and LSTM

Year: 2025

Type: article

Abstract: This article uses monthly observation data from national level ground meteorological observation stations in Beijing from 2014 to 2022, and uses LSTM model and ARIMA time series model to predict the data of air quality index factor indicators. A BP neural network model is used to construct a prediction model for Beijing's air quality index. By comparing the predicted data of Air Quality Index (AQI) with the actual data, the accuracy of the model's predicted AQI in Beijing is over 95%, indicating a relatively accurate prediction accuracy. It can be used for predicting the air quality index in Beijing. Provide technical support for air quality and environmental governance in Beijing.

Source: Highlights in Science Engineering and Technology

DOI: 10.54097/6wv97t44

Authors: Yishuang Li, Zhimin Lin, Jiayuan Jin, Deshao Kong

Institution: Lanzhou Jiaotong University

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: diamond

OpenAlex ID: W4410637337

FWCI:

Cites: 10

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Related to: 10