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

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.

An ensemble learning based hybrid model and framework for air pollution forecasting

2020 ◽  
Vol 27 (30) ◽  
pp. 38155-38168
Author(s):  
Yue-Shan Chang ◽  
Satheesh Abimannan ◽  
Hsin-Ta Chiao ◽  
Chi-Yeh Lin ◽  
Yo-Ping Huang
Keyword(s):  
Air Pollution ◽  
Ensemble Learning ◽  
Hybrid Model ◽  
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 Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Li-Hsin Cheng ◽  
Te-Cheng Hsu ◽  
Che Lin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Selection ◽  
Systems Biology ◽  
Ensemble Learning ◽  
Microarray Data ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Biological Knowledge ◽  
Prognosis Prediction

AbstractBreast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

Research on air pollution system based on neural network

2020 ◽  
pp. 1-11
Author(s):  
Zhiqi Jiang ◽  
Xidong Wang
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Image Features ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
System Model ◽  
Process Data ◽  
Deep Convolutional Neural Networks ◽  
Air Pollution Monitoring ◽  
Neural Network Algorithm

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.

scholarly journals Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis

2014 ◽  
Vol 5 (4) ◽  
pp. 696-708 ◽  
Author(s):  
Madhavi Anushka Elangasinghe ◽  
Naresh Singhal ◽  
Kim N. Dirks ◽  
Jennifer A. Salmond
Keyword(s):  
Air Pollution ◽  
Sensitivity Analysis ◽  
Explicit Knowledge ◽  
Forecasting System ◽  
Air Pollution Forecasting
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