scholarly journals The pollutant concentration prediction model of NNP-BPNN based on the INI algorithm, AW method and neighbor-PCA

2018 ◽  
Vol 10 (8) ◽  
pp. 3059-3065 ◽  
Author(s):  
Hong Zhao ◽  
Yi Wang ◽  
Jiahui Song ◽  
Ge Gao
Keyword(s):  
Prediction Model ◽  
Pollutant Concentration ◽  
Concentration Prediction

scholarly journals Air Pollutant Concentration Forecasting Using Long Short-Term Memory Based on Wavelet Transform and Information Gain: A Case Study of Beijing

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Bingchun Liu ◽  
Xiaoling Guo ◽  
Mingzhao Lai ◽  
Qingshan Wang
Keyword(s):  
Prediction Model ◽  
Wavelet Decomposition ◽  
Information Gain ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
High Dimensional ◽  
Concentration Data ◽  
Atmospheric Pollutant ◽  
Data Set ◽  
Concentration Prediction

Air pollutant concentration forecasting is an effective way which protects health of the public by the warning of the harmful air contaminants. In this study, a hybrid prediction model has been established by using information gain, wavelet decomposition transform technique, and LSTM neural network, and applied to the daily concentration prediction of atmospheric pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Beijing. First, the collected raw data are selected by feature selection by information gain, and a set of factors having a strong correlation with the prediction is obtained. Then, the historical time series of the daily air pollutant concentration is decomposed into different frequencies by using a wavelet decomposition transform and recombined into a high-dimensional training data set. Finally, the LSTM prediction model is trained with high-dimensional data sets, and the parameters are adjusted by repeated tests to obtain the optimal prediction model. The data used in this study were derived from six air pollution concentration data in Beijing from 1/1/2014 to 31/12/2016, and the atmospheric pollutant concentration data of Beijing between 1/1/2017 and 31/12/2017 were used to test the predictive ability of the data set test model. The results show that the evaluation index MAPE of the model prediction is 7.45%. Therefore, the hybrid prediction model has a higher value of application for atmospheric pollutant concentration prediction, because this model has higher prediction accuracy and stability for future air pollutant concentration prediction.

scholarly journals QSPR/QSAR Modelling of the Antioxidant Properties of Some Flavonoids

2021 ◽  
pp. 19-34
Author(s):  
R. Chabi Doco ◽  
M. T. A. Kpota Houngue ◽  
Urbain A. Kuevi ◽  
Y. G. S. Atohoun
Keyword(s):  
Prediction Model ◽  
Redox Potential ◽  
Inhibitory Concentration ◽  
Prediction Models ◽  
Antioxidant Properties ◽  
Bioactive Molecules ◽  
Significant Advance ◽  
Quantum Chemical Methods ◽  
Bioactive Substance ◽  
Concentration Prediction

Several methods exist when seeking to experimentally evaluate the antioxidant properties of a natural bioactive substance. In the case of flavonoids, the methods used are mainly based on the experimental determination of the percentage of inhibition (IC50) or the redox potential (E). In the present work, a prediction study of the redox potential E and the inhibitory concentration LogIC50 was carried out, using the AM1 and HF/6-311G(d,p) method. At the end of this study, three (03) QSPR models were validated and retained, one (01) for the prediction of the redox potential and four (02) for the prediction of the inhibitory concentration : The Redox Prediction Model, developed at the AM1 approximation level, for which 96.43 of the experimental variance is explained by the descriptors : E= -0,29 + 0,22EHomo + 0,11ELumo - 0,05 The Inhibitory Concentration Prediction Models, developed at the AM1 level, for which 96.35⁒ of the experimental variance is explained by the descriptors : LogIC50 = -4,92 + 11,37EHomo + 34,36ELumo + 0,67 The Inhibitory Concentration Prediction Model, developed at the HF/6-311G level (d, p), for which 99.96⁒ of the experimental variance is explained by the descriptors. LogIC50 = 62,40 + 80,25 EHomo - 28,44Elumo + 52,01S - 71,26 η - 6,11μ The development of these QSPR models represents a significant advance in predicting the antioxidant properties of bioactive molecules such as flavonoids based on descriptors calculated by quantum chemical methods.

A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting

2021 ◽  
Vol 16 (4) ◽  
pp. 35-51
Author(s):  
Bingchun Liu ◽  
Xiaogang Yu ◽  
Qingshan Wang ◽  
Shijie Zhao ◽  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Maximum Temperature ◽  
Optimal Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Concentration Prediction

NO2 pollution has caused serious impact on people's production and life, and the management task is very difficult. Accurate prediction of NO2 concentration is of great significance for air pollution management. In this paper, a NO2 concentration prediction model based on long short-term memory neural network (LSTM) is constructed with daily NO2 concentration in Beijing as the prediction target and atmospheric pollutants and meteorological factors as the input indicators. Firstly, the parameters and architecture of the model are adjusted to obtain the optimal prediction model. Secondly, three different sets of input indicators are built on the basis of the optimal prediction model to enter the model learning. Finally, the impact of different input indicators on the accuracy of the model is judged. The results show that the LSTM model has high application value in NO2 concentration prediction. The maximum temperature and O3 among the three input indicators improve the prediction accuracy while the NO2 historical low-frequency data reduce the prediction accuracy.

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