The application of strategy based on LSTM for the short-term prediction of PM2.5 in city-Reference-Cited by-同舟云学术

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The application of strategy based on LSTM for the short-term prediction of PM2.5 in city

Published:2024-01 Issue: Volume:906 Page:167892
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Lin Min-Der,Liu Ping-Yu,Huang Chi-Wei,Lin Yu-Hao

Funder

National Science and Technology Council

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference41 articles.

1. PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network;Chae;Sci. Rep.,2021

2. Identification of environmental microorganism using optimally fine-tuned convolutional neural network;Chen;Environ. Res.,2022

3. Investigation of the main PM2.5 sources and diffusion patterns and corresponding meteorological conditions by the wavelet analysis approach;Chi;Atmos. Pollut. Res.,2021

4. Applying PCA to deep learning forecasting models for predicting PM2. 5;Choi;Sustainability,2021

5. Probing air pollution in the Taichung metropolitan area, Taiwan.Part 1: comprehensive model evaluation and the spatial-temporal evolution of a PM2.5 pollution event;Chuang;Atmos. Res.,2023

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3. Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model;Atmospheric Environment;2024-08

4. Incorporating the Third Law of Geography with Spatial Attention Module–Convolutional Neural Network–Transformer for Fine-Grained Non-Stationary Air Quality Predictive Learning;Mathematics;2024-05-08

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