scholarly journals On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

2020 ◽  
Vol 5 (2) ◽  
pp. 512-526 ◽  
Author(s):  
Ola Surakhi ◽  
Sami Serhan ◽  
Imad Salah
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Recurrent Neural Network ◽  
Air Pollution Forecasting

scholarly journals Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecasting

2021 ◽  
Author(s):  
Van-Duc Le
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
State Of The Art ◽  
Influential Factors ◽  
Temporal Features ◽  
Air Pollution Forecasting

This paper applies a Spatiotemporal Graph Convolutional Recurrent Neural Network which is a tight combination of a Graph Neural Network (GNN) to a Recurrent Neural Network (RNN) architecture for air pollution forecasting in long-term for the entire city. Our model can effectively learn the spatial and temporal features of the air pollution data and its influential factors (e.g. weather, traffic, external areas) at the time. Our method achieves better performance than a state-of-the-art ConvLSTM model in air pollution forecasting and a hybrid GNN-based model that separates GNN and RNN in discrete layers.

scholarly journals Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecasting

2021 ◽  
Author(s):  
Van-Duc Le
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
State Of The Art ◽  
Influential Factors ◽  
Temporal Features ◽  
Air Pollution Forecasting

This paper applies a Spatiotemporal Graph Convolutional Recurrent Neural Network which is a tight combination of a Graph Neural Network (GNN) to a Recurrent Neural Network (RNN) architecture for air pollution forecasting in long-term for the entire city. Our model can effectively learn the spatial and temporal features of the air pollution data and its influential factors (e.g. weather, traffic, external areas) at the time. Our method achieves better performance than a state-of-the-art ConvLSTM model in air pollution forecasting and a hybrid GNN-based model that separates GNN and RNN in discrete layers.

scholarly journals A Layered Recurrent Neural Network for Imputing Air Pollutants Missing Data and Prediction of NO 2, O 3, PM 10, and PM 2.5

2020 ◽  
Author(s):  
Hamza Turabieh ◽  
Alaa Sheta ◽  
Malik Braik ◽  
Elvira Kovač-Andrić
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Air Quality ◽  
Recurrent Neural Network ◽  
Missing Values ◽  
Prediction Models ◽  
Air Pollutant ◽  
Forecasting Models ◽  
Monitoring Strategies ◽  
Artificial Neural Network Ann

To fulfill the national air quality standards, many countries have created emissions monitoring strategies on air quality. Nowadays, policymakers and air quality executives depend on scientific computation and prediction models to monitor that cause air pollution, especially in industrial cities. Air pollution is considered one of the primary problems that could cause many human health problems such as asthma, damage to lungs, and even death. In this study, we present investigated development forecasting models for air pollutant attributes including Particulate Matters (PM2.5, PM10), ground-level Ozone (O3), and Nitrogen Oxides (NO2). The dataset used was collected from Dubrovnik city, which is located in the east of Croatia. The collected data has missing values. Therefore, we suggested the use of a Layered Recurrent Neural Network (L-RNN) to impute the missing value(s) of air pollutant attributes then build forecasting models. We adopted four regression models to forecast air pollutant attributes, which are: Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Artificial Neural Network (ANN) and L-RNN. The obtained results show that the proposed method enhances the overall performance of other forecasting models.

Air pollution forecasting based on attention‐based LSTM neural network and ensemble learning

2019 ◽  
Vol 37 (3) ◽  
Author(s):  
Duen‐Ren Liu ◽  
Shin‐Jye Lee ◽  
Yang Huang ◽  
Chien‐Ju Chiu
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Ensemble Learning ◽  
Air Pollution Forecasting

Air Pollution Prediction Using Recurrent Neural Network, Long Short-Term Memory and Hybrid of Convolutional Neural Network and Long Short-Term Memory Models

2020 ◽  
Vol 17 (9) ◽  
pp. 4580-4584
Author(s):  
Naresh Kumar ◽  
Jatin Bindra ◽  
Rajat Sharma ◽  
Deepali Gupta
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Air Pollution Prediction ◽  
Pollution Prediction

Air pollution prediction was not an easy task few years back. With the increasing computation power and wide availability of the datasets, air pollution prediction problem is solved to some extend. Inspired by the deep learning models, in this paper three techniques for air pollution prediction have been proposed. The models used includes recurrent neural network (RNN), Long short-term memory (LSTM) and a hybrid combination of Convolutional neural network (CNN) and LSTM models. These models are tested by comparing MSE loss on air pollution test of Belgium. The validation loss on RNN is 0.0045, LSTM is 0.00441 and CNN and LSTM is 0.0049. The loss on testing dataset for these models are 0.00088, 0.00441 and 0.0049 respectively.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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