Long Short Term Memory Model for Analysis and Forecast of PM2.5

Yan, Lianshan; Zhou, Min; Wu, Yaowen; Yan, Luqi

doi:10.1007/978-3-030-00006-6_57

Cited by 5 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to establish the efficacy of the proposed MSAFormer model, it was juxtaposed against five widely recognized models: Support Vector Machine (SVM) [30], Random Forest (RF) [32], Adaptive Boosting (AdaBoost) [34], Long Short-Term Memory (LSTM) [43], and Gated Recurrent Unit (GRU) [46]. These models are detailed below:…”

Section: Models Comparation and Performance Analysismentioning

confidence: 99%

“…Recent widespread application of deep learning technologies is beginning to alter this situation [40][41][42]. Researchers have started integrating meteorological variables into the deep learning models for PM 2.5 prediction, but the prevalent approach still relies on manual feature design and employs relatively traditional models such as Long Short-Term Memory (LSTM) [43][44][45] and Gated Recurrent Units (GRU) [46,47]. While these models exhibit strengths in handling the time-series characteristics of meteorological data, their capacity to excavate deep and complex features in multi-source meteorological data needs further enhancement [48,49].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

Wang

Zhang

2023

Atmosphere

View full text Add to dashboard Cite

The accurate prediction of PM2.5 concentration, a matter of paramount importance in environmental science and public health, has remained a substantial challenge. Conventional methodologies for predicting PM2.5 concentration often grapple with capturing complex dynamics and nonlinear relationships inherent in multi-station meteorological data. To address this issue, we have devised a novel deep learning model, named the Meteorological Sparse Autoencoding Transformer (MSAFormer). The MSAFormer leverages the strengths of the Transformer architecture, effectively incorporating a Meteorological Sparse Autoencoding module, a Meteorological Positional Embedding Module, and a PM2.5 Prediction Transformer Module. The Sparse Autoencoding Module serves to extract salient features from high-dimensional, multi-station meteorological data. Subsequently, the Positional Embedding Module applies a one-dimensional Convolutional Neural Network to flatten the sparse-encoded features, facilitating data processing in the subsequent Transformer module. Finally, the PM2.5 Prediction Transformer Module utilizes a self-attention mechanism to handle temporal dependencies in the input data, predicting future PM2.5 concentrations. Experimental results underscore that the MSAFormer model achieves a significant improvement in predicting PM2.5 concentrations in the Haidian district compared to traditional methods. This research offers a novel predictive tool for the field of environmental science and illustrates the potential of deep learning in the analysis of environmental meteorological data.

show abstract

Section: Models Comparation and Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

Wang

Zhang

2023

Atmosphere

View full text Add to dashboard Cite

show abstract

“…Though, the problem occurs when the time interval increase, the exactness of forecast is getting lower. In addition to the previous two studies, another study made the analysis and forecasted PM2.5 due to its correlation with air pollution [16]. It used three models: Random Forest, Encoder-Decoder, and LSTM model.…”

Section: Deep Learning Approachmentioning

confidence: 99%

Air Pollution Forecasting Using Deep Learning

et al. 2021

View full text Add to dashboard Cite

Nowadays, air pollution is getting an extreme problem that affects the whole environment. Due to the dangerous effects of air pollution on human’s health, this study proposes an air pollution prediction system. Because of the high dust pollution in Saudi Arabia, and the fact that there is no system for predicting the percentage of air pollution in it, this study applies an air pollution prediction system to the most affected area in Saudi Arabia. This paper aims to forecast the concentrations of PM10 particles due to their dangerous effects. This study aims to align with the Saudi vision 2030 by having an ideal environment and act in an efficient way in case of a warning situation. It applies a deep learning technique, which called Long Short-Term Memory (LSTM) to predict the air pollution in Saudi Arabia and achieved exceptional results due to the low error rates that have been obtained by this study. The error rate of Mean Absolute Error (MAE) is 0.98, for Root Mean Square Error (RMSE) is 8.68 and 0.999 for R-Squared.

show abstract

“…Hajiloo et al [23] employed geographical weight regression (GWR) to investigate impact of meteorological and environmental parameters on PM2.5 concentrations in Tehran, Iran. Yang et al [24] quantified the influence of natural and socioeconomic factors on PM2.5 pollution using the GeoDetector model [25,26].…”

Section: Introductionmentioning

confidence: 99%

Exploring Spatial Influence of Remotely Sensed PM2.5 Concentration Using a Developed Deep Convolutional Neural Network Model

Jin

2019

IJERPH

View full text Add to dashboard Cite

Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for exploiting the spatial influence feature in remotely sensed data. The method was applied in investigating the magnitude of the spatial influence of four factors—population, gross domestic product (GDP), terrain, land-use and land-cover (LULC)—on remotely sensed PM2.5 concentration over China. Satisfactory results were produced by the method. It demonstrates that the deep CNN model can be well applied in the field of spatial analysing remotely sensed big data. And the accuracy of the deep CNN is much higher than of geographically weighted regression (GWR) based on comparation. The results showed that population spatial density, GDP spatial density, terrain, and LULC could together determine the spatial distribution of PM2.5 annual concentrations with an overall spatial influencing magnitude of 97.85%. Population, GDP, terrain, and LULC have individual spatial influencing magnitudes of 47.12% and 36.13%, 50.07% and 40.91% on PM2.5 annual concentrations respectively. Terrain and LULC are the dominating spatial influencing factors, and only these two factors together may approximately determine the spatial pattern of PM2.5 annual concentration over China with a high spatial influencing magnitude of 96.65%.

show abstract

Long Short Term Memory Model for Analysis and Forecast of PM2.5

Cited by 5 publications

References 10 publications

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

Air Pollution Forecasting Using Deep Learning

Exploring Spatial Influence of Remotely Sensed PM2.5 Concentration Using a Developed Deep Convolutional Neural Network Model

Contact Info

Product

Resources

About