scholarly journals Air Quality Prediction Using Improved PSO-BP Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 99346-99353
Author(s):  
Yuan Huang ◽  
Yuxing Xiang ◽  
Ruixiao Zhao ◽  
Zhe Cheng
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Bp Neural Network ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Improved Pso

scholarly journals An Unified Method of Exploration-Based Air Quality Prediction

2020 ◽  
Vol 8 (6) ◽  
pp. 4705-4708
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Bp Neural Network ◽  
Quality Index ◽  
Weather Condition ◽  
Air Quality Index ◽  
Air Quality Prediction ◽  
Large Current ◽  
New Information ◽  
Neural Network Algorithm

Data mining is the application of examining large current databases in sequence to create new information. It is a classification of artificial intelligence build on the concept that systems can get from data, analyze patterns and make judgment with minimal human intervention. The forecast of air quality is done with analyzing the AQI (Air Quality Index) of the atmosphere in different areas. These predictions are done using the BP Neural network Algorithm in which the data of the gases like CO2, CO, SO2, O3, NO2, PM2.5 etc. is first classified in the system, and then the normality is checked by comparison of each gases with the normality. But the prediction cannot be fully excepted because it doesn’t consider the outside weather condition of the atmosphere. This paper uses the ANN (Artificial Neural Network) technique along with BP Neural Network which analysis the weather condition of the atmosphere along with the data of the polluted gases. This paper predict more efficient air quality index of the atmosphere.

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 A Multiple Kernel Learning Approach for Air Quality Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hong Zheng ◽  
Haibin Li ◽  
Xingjian Lu ◽  
Tong Ruan
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Arima Model ◽  
Multiple Kernel Learning ◽  
Parametric Models ◽  
Kernel Learning ◽  
Support Vector ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Multiple Kernel

Air quality prediction is an important research issue due to the increasing impact of air pollution on the urban environment. However, existing methods often fail to forecast high-polluting air conditions, which is precisely what should be highlighted. In this paper, a novel multiple kernel learning (MKL) model that embodies the characteristics of ensemble learning, kernel learning, and representative learning is proposed to forecast the near future air quality (AQ). The centered alignment approach is used for learning kernels, and a boosting approach is used to determine the proper number of kernels. To demonstrate the performance of the proposed MKL model, its performance is compared to that of classical autoregressive integrated moving average (ARIMA) model; widely used parametric models like random forest (RF) and support vector machine (SVM); popular neural network models like multiple layer perceptron (MLP); and long short-term memory neural network. Datasets acquired from a coastal city Hong Kong and an inland city Beijing are used to train and validate all the models. Experiments show that the MKL model outperforms the other models. Moreover, the MKL model has better forecast ability for high health risk category AQ.

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