scholarly journals Multi-Group Encoder-Decoder Networks to Fuse Heterogeneous Data for Next-Day Air Quality Prediction

2019 ◽  
Author(s):  
Yawen Zhang ◽  
Qin Lv ◽  
Duanfeng Gao ◽  
Si Shen ◽  
Robert Dick ◽  
...  
Keyword(s):  
Air Quality ◽  
Heterogeneous Data ◽  
Air Pollutant ◽  
Weather Data ◽  
Quality Prediction ◽  
Prevention Measures ◽  
Convolution Operators ◽  
Real World Data ◽  
Air Quality Prediction ◽  
Cross Domain

Accurate next-day air quality prediction is essential to enable warning and prevention measures for cities and individuals to cope with potential air pollution, such as vehicle restriction, factory shutdown, and limiting outdoor activities. The problem is challenging because air quality is affected by a diverse set of complex factors. There has been prior work on short-term (e.g., next 6 hours) prediction, however, there is limited research on modeling local weather influences or fusing heterogeneous data for next-day air quality prediction. This paper tackles this problem through three key contributions: (1) we leverage multi-source data, especially high-frequency grid-based weather data, to model air pollutant dynamics at station-level; (2) we add convolution operators on grid weather data to capture the impacts of various weather parameters on air pollutant variations; and (3) we automatically group (cross-domain) features based on their correlations, and propose multi-group Encoder-Decoder networks (MGED-Net) to effectively fuse multiple feature groups for next-day air quality prediction. The experiments with real-world data demonstrate the improved prediction performance of MGED-Net over state-of-the-art solutions (4.2% to 9.6% improvement in MAE and 9.2% to 16.4% improvement in RMSE).

scholarly journals A DBN-Based Deep Neural Network Model with Multitask Learning for Online Air Quality Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jiangeng Li ◽  
Xingyang Shao ◽  
Rihui Sun
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Deep Neural Network ◽  
Multitask Learning ◽  
Air Pollutant ◽  
Quality Prediction ◽  
Air Quality Prediction

To avoid the adverse effects of severe air pollution on human health, we need accurate real-time air quality prediction. In this paper, for the purpose of improve prediction accuracy of air pollutant concentration, a deep neural network model with multitask learning (MTL-DBN-DNN), pretrained by a deep belief network (DBN), is proposed for forecasting of nonlinear systems and tested on the forecast of air quality time series. MTL-DBN-DNN model can solve several related prediction tasks at the same time by using shared information contained in the training data of different tasks. In the model, DBN is used to learn feature representations. Each unit in the output layer is connected to only a subset of units in the last hidden layer of DBN. Such connection effectively avoids the problem that fully connected networks need to juggle the learning of each task while being trained, so that the trained networks cannot get optimal prediction accuracy for each task. The sliding window is used to take the recent data to dynamically adjust the parameters of the MTL-DBN-DNN model. The MTL-DBN-DNN model is evaluated with a dataset from Microsoft Research. Comparison with multiple baseline models shows that the proposed MTL-DBN-DNN achieve state-of-art performance on air pollutant concentration forecasting.

scholarly journals Research on the air quality prediction model of Wuhai mining area based on deep learning

2021 ◽  
Vol 300 ◽  
pp. 02005
Author(s):  
Jinghua Wang ◽  
Jin Cheng ◽  
Fang Liu ◽  
Lei Yan ◽  
Taijie Tang
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Large Scale ◽  
Prediction Models ◽  
Mining Area ◽  
Air Pollutant ◽  
Quality Prediction ◽  
Weather Factors ◽  
Air Quality Prediction ◽  
Deep Recurrent Neural Network

With the large-scale and high-intensity mining of coal resources in the Wuhai mining area, the destruction of soil and erosion of rocks has intensified, causing a large amount of surface soil spalling from the mine body and serious damage to the surface vegetation, which has had a serious impact on the quality of the environment in and around the mine. This paper focuses on the corresponding early warning research on air quality in the mining area of Wuhai, and constructs Deep Recurrent Neural Network (DRNN) and Deep Long Short Time Memory Neural Network (DLSTM) air quality prediction models based on the filtered weather factors. The simulation results are also compared and find that the prediction results of DLSTM are better than those of DRNN, with a prediction accuracy of 92.85%. The model is able to accurately predict the values and trends of various air pollutant concentrations in the mining area of Wuhai.

Deep spatial-temporal fusion network for fine-grained air pollutant concentration prediction

2021 ◽  
Vol 25 (2) ◽  
pp. 419-438
Author(s):  
Liang Ge ◽  
Kunyan Wu ◽  
Feng Chang ◽  
Aoli Zhou ◽  
Hang Li ◽  
...  
Keyword(s):  
Air Quality ◽  
Environmental Governance ◽  
Tensor Decomposition ◽  
Heterogeneous Data ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Air Quality Prediction ◽  
Region Selection ◽  
Similar Region ◽  
Data Completion

Air pollution is a serious environmental problem that has attracted much attention. Predicting air pollutant concentration can provide useful information for urban environmental governance decision-making and residents’ daily health control. However, existing methods fail to model the temporal dependencies or have suffer from a weak ability to capture the spatial correlations of air pollutants. In this paper, we propose a general approach to predict air pollutant concentration, named DSTFN, which consists of a data completion component, a similar region selection component, and a deep spatial-temporal fusion network. The data completion component uses tensor decomposition method to complete the missing data of historical air quality. The similar region selection component uses region metadata to calculate the spatial similarity between regions. The deep spatial-temporal fusion network fuses urban heterogeneous data to capture factors affecting air quality and predict air pollutant concentration. Extensive experiments on a real-world dataset demonstrate that our model achieves the highest performance compared with state-of-the-art models for air quality prediction.

scholarly journals Air Quality Prediction using Artificial Intelligence

2020 ◽  
Vol 9 (5) ◽  
pp. 1417-1420
Keyword(s):  
Artificial Intelligence ◽  
Air Quality ◽  
Urban Areas ◽  
Specific Area ◽  
Air Pollutant ◽  
Support Vector ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Pollutant Concentrations ◽  
Spatio Temporal

Due to the critical impacts of air pollution, prediction and monitoring of air quality in urban areas are essential tasks. However, because of the dynamic nature and high Spatio-temporal variability, the prediction of the air pollutant concentrations is a complex Spatio-temporal problem. The data is collected in specific area such as climate condition and vehicular pollutant occurring in the peak hours. the predication process is used to compare the algorithm artificial neural network and support vector machine process. This paper presents a survey on Air quality prediction using artificial intelligence

scholarly journals Research on air quality prediction method in Hangzhou based on machine learning

2021 ◽  
Vol 2010 (1) ◽  
pp. 012011
Author(s):  
Zhongjie Fu ◽  
Haiping Lin ◽  
Bingqiang Huang ◽  
Jiana Yao
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Prediction Method ◽  
Quality Prediction ◽  
Air Quality Prediction
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