Air Pollution Concentration Forecast Method Based on the Deep Ensemble Neural Network
المؤلفون المشاركون
Guo, Canyang
Liu, Genggeng
Chen, Chi-Hua
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-10-05
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
The global environment has become more polluted due to the rapid development of industrial technology.
However, the existing machine learning prediction methods of air quality fail to analyze the reasons for the change of air pollution concentration because most of the prediction methods take more focus on the model selection.
Since the framework of recent deep learning is very flexible, the model may be deep and complex in order to fit the dataset.
Therefore, overfitting problems may exist in a single deep neural network model when the number of weights in the deep neural network model is large.
Besides, the learning rate of stochastic gradient descent (SGD) treats all parameters equally, resulting in local optimal solution.
In this paper, the Pearson correlation coefficient is used to analyze the inherent correlation of PM2.5 and other auxiliary data such as meteorological data, season data, and time stamp data which are applied to cluster for enhancing the performance.
Extracted features are helpful to build a deep ensemble network (EN) model which combines the recurrent neural network (RNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) network to predict the PM2.5 concentration of the next hour.
The weights of the submodel change with the accuracy of them in the validation set, so the ensemble has generalization ability.
The adaptive moment estimation (Adam) an algorithm for stochastic optimization is used to optimize the weights instead of SGD.
In order to compare the overall performance of different algorithms, the mean absolute error (MAE) and mean absolute percentage error (MAPE) are used as accuracy metrics in the experiments of this study.
The experiment results show that the proposed method achieves an accuracy rate (i.e., MAE=6.19 and MAPE=16.20%) and outperforms the comparative models.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Guo, Canyang& Liu, Genggeng& Chen, Chi-Hua. 2020. Air Pollution Concentration Forecast Method Based on the Deep Ensemble Neural Network. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1214743
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Guo, Canyang…[et al.]. Air Pollution Concentration Forecast Method Based on the Deep Ensemble Neural Network. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1214743
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Guo, Canyang& Liu, Genggeng& Chen, Chi-Hua. Air Pollution Concentration Forecast Method Based on the Deep Ensemble Neural Network. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1214743
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1214743
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