An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network
المؤلفون المشاركون
Cai, Jianxian
Dai, Xun
Hong, Li
Gao, Zhitao
Qiu, Zhongchao
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-15
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Aiming at remedying the problem of low prediction accuracy of existing air pollutant prediction models, a denoising autoencoder deep network (DAEDN) model that is based on long short-term memory (LSTM) networks was designed.
This model created a noise reduction autoencoder with an LSTM network to extract the inherent air quality characteristics of original monitoring data and to implement noise reduction processing on monitoring data to improve the accuracy of air quality predictions.
The LSTM network structure in the DAEDN model was designed as bidirectional LSTM (Bi-LSTM) to solve the problem of a lag in the unidirectional LSTM prediction results and thereby to further improve the prediction accuracy of the prediction model.
Using air pollutant time series data, the DAEDN model was trained using hourly PM2.5 concentration data collected in Beijing over 5 years.
The experimental results show that the DAEDN model can extract more stable features from the noisy input after training was completed.
The models were evaluated using RMSE and MAE, and the results show that the indexes are 15.504 and 6.789; compared with unidirectional LSTM, it is reduced by 7.33% and 5.87%, respectively.
In addition, the new prediction model essentially considered the time series properties of the prediction of the concentration of spatial pollutants and the fully integrated environmental big data, such as air quality monitoring, meteorological monitoring, and forecasting.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Cai, Jianxian& Dai, Xun& Hong, Li& Gao, Zhitao& Qiu, Zhongchao. 2020. An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1194441
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Cai, Jianxian…[et al.]. An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1194441
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Cai, Jianxian& Dai, Xun& Hong, Li& Gao, Zhitao& Qiu, Zhongchao. An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1194441
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1194441
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر