Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence
Abstract
:1. Introduction
Increase in Air Pollutants and Asthma
2. Related Research
2.1. Effects of Air Pollutants on Asthma
2.2. Effects of Meteorological Changes on Asthma
2.3. Prediction of the Asthmatic Occurrence
3. Materials and Methods
3.1. Datasets
3.1.1. Asthmatic Occurrence
3.1.2. Atmospheric Environments
3.1.3. Meteorological Environments
3.2. Methodology
3.2.1. Vector Autoregressive Model
3.2.2. Hybrid Deep-Learning Model Based VAR & DNN
4. Results
4.1. Vector Autoregressive Model
4.1.1. Unit Root Test
4.1.2. Granger Causality
4.1.3. Estimation of VAR Model
4.2. Hybrid Deep Neural Networks
4.2.1. Extract Features from VAR Model
4.2.2. Performance Evaluation of Hybrid DNN
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Variables | Mean | Median | Kurtosis | Skewness | Percentiles | |||
---|---|---|---|---|---|---|---|---|
Min | 25th | 75th | Max | |||||
Occurrence | 5859.59 | 5737.00 | 0.17 | 0.56 | 2699.00 | 4728.00 | 6724.00 | 11,881.00 |
Variables | Mean | Median | Kurtosis | Skewness | Percentiles | |||
---|---|---|---|---|---|---|---|---|
Min | 25th | 75th | Max | |||||
SO2 (ppm) | 0.005 | 0.005 | 3.321 | 1.216 | 0.003 | 0.004 | 0.006 | 0.013 |
CO (ppm) | 0.564 | 0.520 | 1.388 | 1.217 | 0.280 | 0.443 | 0.636 | 1.251 |
O3 (ppm) | 0.020 | 0.020 | −0.377 | 0.374 | 0.003 | 0.012 | 0.027 | 0.053 |
NO2 (ppm) | 0.037 | 0.036 | 0.084 | 0.547 | 0.014 | 0.028 | 0.045 | 0.082 |
PM10 (1 µg/m3) | 46.763 | 43.393 | 122.845 | 7.150 | 7.975 | 31.768 | 57.227 | 555.692 |
PM2.5 (1 µg/m3) | 24.348 | 22.154 | 2.249 | 1.229 | 3.275 | 15.373 | 30.378 | 87.821 |
Variables | Mean | Median | Kurtosis | Skewness | Percentiles | |||
---|---|---|---|---|---|---|---|---|
Min | 25th | 75th | Max | |||||
Temperature (°C) | 13.28 | 14.40 | −1.06 | −0.27 | 14.80 | 3.50 | 23.10 | 33.70 |
Humidity (%) | 58.54 | 58.60 | −0.36 | 0.15 | 21.80 | 47.60 | 68.10 | 97.00 |
Air Pressure (hPa) | 1006.09 | 1006.70 | −0.66 | −0.07 | 981.0 | 999.60 | 1012.50 | 1026.20 |
Variables | At the Level | First Difference | |||
---|---|---|---|---|---|
t-Statistics | p-Value | t-Statistics | p-Value | ||
Asthmatic Occurrence | AsO | −2.8965 | 0.0457 ** | −2.8965 | 0.0457 ** |
Atmospheric Environment | SO2 | −4.5497 | 0.0001 *** | −4.5497 | 0.0001 *** |
CO | −4.0074 | 0.0013 ** | −4.0074 | 0.0013 ** | |
O3 | −2.7408 | 0.0672 * | −11.6995 | <0.0001 *** | |
NO2 | −8.5039 | <0.0001 *** | −8.5039 | <0.0001 *** | |
PM10 | −5.4865 | <0.0001 *** | −5.4865 | <0.0001 *** | |
PM2.5 | −8.7544 | <0.0001 *** | −8.7544 | <0.0001 *** | |
Meteorological Environment | Temp | −2.2999 | 0.1719 | −4.3413 | 0.0003 *** |
Hum | −4.2481 | 0.0005 *** | −4.2481 | 0.0005 *** | |
AiPr | −2.0903 | 0.2484 | −12.9901 | <0.0001 *** |
Dependent Variables | AsO | SO2 | CO | O3 | NO2 | PM10 | PM2.5 | Temp | Hum | AiPr |
---|---|---|---|---|---|---|---|---|---|---|
AsO | 1.00 | <0.01 *** | <0.01 *** | 0.21 | <0.01 *** | 0.03 ** | <0.01 *** | <0.01 *** | <0.01 *** | 0.05 ** |
SO2 | <0.01 *** | 1.00 | <0.01 *** | 0.08 * | <0.01 *** | 0.14 | <0.01 *** | <0.01 *** | <0.01 *** | <0.01 *** |
CO | <0.01 *** | 0.23 | 1.00 | 0.05 ** | <0.01 *** | 0.10 * | <0.01 *** | <0.01 *** | <0.01 *** | <0.01 *** |
O3 | 0.35 | 0.07 * | <0.01 *** | 1.00 | <0.01 *** | 0.72 | 0.15 | <0.01 *** | <0.01 *** | <0.01 *** |
NO2 | <0.01 *** | <0.01 *** | 0.06 * | <0.01 *** | 1.00 | 0.07 * | 0.19 | <0.01 *** | <0.01 *** | <0.01 *** |
PM10 | <0.01 *** | <0.01 *** | <0.01 *** | 0.40 | <0.01 *** | 1.00 | <0.01 *** | <0.01 *** | <0.01 *** | 0.36 |
PM2.5 | 0.01 *** | <0.01 *** | <0.01 *** | 0.03 ** | <0.01 *** | 0.04 ** | 1.00 | <0.01 *** | <0.01 *** | <0.01 *** |
Temp | 0.06 * | <0.01 ** | <0.01 *** | <0.01 *** | <0.01 *** | 0.17 | <0.01 *** | 1.00 | 0.03 ** | <0.01 *** |
Hum | <0.01 *** | <0.01 *** | <0.01 *** | <0.01 *** | <0.01 *** | <0.01 *** | <0.01 *** | <0.01 *** | 1.00 | <0.01 *** |
AiPr | 0.40 | <0.01 *** | <0.01 *** | <0.01 *** | <0.01 ** | 0.04 ** | 0.02 ** | <0.01 *** | <0.01 *** | 1.00 |
Lag | AIC | BIC | HQIC |
---|---|---|---|
0 | −5.454 | −5.140 | −5.333 |
1 | −8.327 | −7.386 * | −7.964 |
2 | −8.582 | −7.014 | −7.977 * |
3 | −8.726 | −6.531 | −7.879 |
4 | −8.828 | −6.006 | −7.740 |
5 | −9.136 | −5.686 | −7.805 |
6 | −9.219 * | −5.142 | −7.646 |
7 | −9.137 | −4.434 | −7.323 |
8 | −9.026 | −3.695 | −6.970 |
9 | −8.952 | −2.994 | −6.654 |
10 | −8.869 | −2.284 | −6.329 |
Variables | Coefficient | t-Statistics | p-Value | VIF |
---|---|---|---|---|
2138.343 | 6.425 | <0.001 *** | − | |
−1717.088 | 26.210 | <0.001 *** | 1.190 | |
−1359.116 | −11.449 | <0.001 *** | 2.528 | |
−275.489 | −2.571 | 0.010 ** | 1.980 | |
174.505 | 1.449 | 0.147 | 2.560 | |
9.245 | 1.204 | 0.229 | 1.153 | |
2575.587 | 0.558 | 0.577 | 1.171 | |
−54.148 | −4.059 | <0.001 *** | 1.317 | |
−4.435 | −0.849 | 0.396 | 4.271 | |
292.689 | 0.551 | 0.581 | 7.890 | |
−0.995 | −0.568 | 0.570 | 2.645 | |
3.472 | 1.191 | 0.234 | 1.666 | |
12,668.225 | 1.865 | 0.062 * | 5.915 | |
95,442.851 | 2.062 | 0.039 ** | 2.670 | |
0.383 | 14.864 | <0.001 *** | 2.090 |
Model | Learning Rate | Epoch | Performance Evaluation | |||
---|---|---|---|---|---|---|
MAE | MSE | RMSE | MAPE | |||
Hybrid DNN | 0.001 | 300 | 479.31 | 473,295.40 | 687.96 | 8.20 |
VAR | − | − | 668.50 | 71,750.20 | 847.20 | 13.17 |
DNN | 0.001 | 300 | 691.22 | 818,571.94 | 904.75 | 11.87 |
LSTM | 0.001 | 100 | 821.72 | 1140,624.00 | 1068.00 | 15.01 |
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Kim, M.-S.; Lee, J.-H.; Jang, Y.-J.; Lee, C.-H.; Choi, J.-H.; Sung, T.-E. Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence. Sustainability 2020, 12, 6143. https://doi.org/10.3390/su12156143
Kim M-S, Lee J-H, Jang Y-J, Lee C-H, Choi J-H, Sung T-E. Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence. Sustainability. 2020; 12(15):6143. https://doi.org/10.3390/su12156143
Chicago/Turabian StyleKim, Min-Seung, Jeong-Hee Lee, Yong-Ju Jang, Chan-Ho Lee, Ji-Hye Choi, and Tae-Eung Sung. 2020. "Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence" Sustainability 12, no. 15: 6143. https://doi.org/10.3390/su12156143