PM2.5 concentration modeling and prediction by using temperature-based deep belief network

Xing, Haixia; Wang, Gongming; Liu, Caixia; Suo, Minghe

doi:10.1016/j.neunet.2020.10.013

Cited by 49 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A random bonding model (RBM) is the basic component unit of a DBN, which is a Markov random field with hidden nodes. 16 Each RBM is a two-layer network consists of a visible layer ν and a hidden layer ℎ. The nodes in the same layer are independent from each other.…”

Section: Theoretical Basis Of the Prediction Modelmentioning

confidence: 99%

Soil Heavy Metal Content Prediction Based on a Deep Belief Network and Random Forest Model

et al. 2022

View full text Add to dashboard Cite

In order to extract useful information from XRF (X-ray fluorescence) spectrum, and establish a high-accuracy prediction model of soil heavy metal contents, a hybrid model combined Deep Belief Network (DBN) with tree-based model was proposed. The DBN was firstly introduced into feature extraction of XRF spectral data, which can obtain deep layer features of spectrum. Owing to the strong regression ability of tree-based model, it can offset the deficiency of DBN in prediction ability, so it was used for predicting heavy metal contents based on the extracted features. In order to further improve the performance of the model, the parameters of model can be optimized according to the prediction error, which was completed by sparrow search algorithm (SSA) and the gird search. The hybrid model was applied to predict the contents of As and Pb based on spectral data of overlapping peaks. It can be obtained that R2 of As and Pb reached 0.9884 and 0.9358, the MSE of As and Pb are as low as 0.0011 and 0.0058, which outperform other commonly used models. That proved the combination of DBN and tree-based model can obtain more accurate prediction results.

show abstract

Section: Theoretical Basis Of the Prediction Modelmentioning

confidence: 99%

Soil Heavy Metal Content Prediction Based on a Deep Belief Network and Random Forest Model

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Pak et al [ 46 ] used the highest and lowest temperature of the daily into the input variables. Xing et al [ 47 ] incorporated mean atmospheric pressure into input variables to make predictions. Polichetti et al [ 48 ] also pointed out that the observed values of relative humidity and wind speed are good indicators for predicting the concentration of PM 2.5 .…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a Spark Big Data Framework for PM2.5 Air Pollution Forecasting

Shih

Nguyen

et al. 2021

IJERPH

View full text Add to dashboard Cite

In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM2.5 is one of the main components of air pollution. Therefore, it is necessary to know the PM2.5 air quality in advance for health. Many studies on air quality are based on the government’s official air quality monitoring stations, which cannot be widely deployed due to high cost constraints. Furthermore, the update frequency of government monitoring stations is once an hour, and it is hard to capture short-term PM2.5 concentration peaks with little warning. Nevertheless, dealing with short-term data with many stations, the volume of data is huge and is calculated, analyzed and predicted in a complex way. This alleviates the high computational requirements of the original predictor, thus making Spark suitable for the considered problem. This study proposes a PM2.5 instant prediction architecture based on the Spark big data framework to handle the huge data from the LASS community. The Spark big data framework proposed in this study is divided into three modules. It collects real time PM2.5 data and performs ensemble learning through three machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting Decision Tree) to predict the PM2.5 concentration value in the next 30 to 180 min with accompanying visualization graph. The experimental results show that our proposed Spark big data ensemble prediction model in next 30-min prediction has the best performance (R2 up to 0.96), and the ensemble model has better performance than any single machine learning model. Taiwan has been suffering from a situation of relatively poor air pollution quality for a long time. Air pollutant monitoring data from LASS community can provide a wide broader monitoring, however the data is large and difficult to integrate or analyze. The proposed Spark big data framework system can provide short-term PM2.5 forecasts and help the decision-maker to take proper action immediately.

show abstract

“…PM2.5 is the primary air pollutant, which turns out to be the focus of China's haze governance at this stage. PM2.5 harms human health, which has been brought to the center of public attention (Xing et al, 2021). If PM2.5 can be accurately predicted, which will have important implications for environmental management and human health.…”

Section: Introductionmentioning

confidence: 99%

Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network

Zhang

Chen²,

Zhao³

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

In the context of low-carbon globalization, green development has become the common pursuit of all countries and the theme of China’s development in the new era. Fine particulate matter (PM2.5) is one of the main challenges affecting air quality, and how to accurately predict PM2.5 plays a pivotal role in environmental governance. However, traditional data-driven approaches and deep learning methods for prediction rarely consider spatiotemporal features. Furthermore, different regions always have various implicit or hidden states, which have rarely been considered in the off-the-shelf model. To solve these problems, this study proposed a novel Spatial-Temporal Matrix Factorization Generative Adversarial Network (ST MFGAN) to capture spatiotemporal correlations and overcome the regional diversity problem at the same time. Specifically, Generative Adversarial Network (GAN) composed of graph Convolutional Network (GCN) and Long-Short-Term Memory (LSTM) network is used to generate a large amount of reliable spatiotemporal data, and matrix factorization network is used to decompose the vector output by GAN into multiple sub-networks. PM2.5 are finally combined and jointly predicted by the fusion layer. Extensive experiments show the superiority of the newly designed method.

show abstract

PM2.5 concentration modeling and prediction by using temperature-based deep belief network

Cited by 49 publications

References 29 publications

Soil Heavy Metal Content Prediction Based on a Deep Belief Network and Random Forest Model

Soil Heavy Metal Content Prediction Based on a Deep Belief Network and Random Forest Model

Design of a Spark Big Data Framework for PM2.5 Air Pollution Forecasting

Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network

Contact Info

Product

Resources

About