با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
دسته بندی:
یادگیری عمیق - deep learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques
ترجمه فارسی عنوان مقاله:
بهبود دقت پیش بینی کیفیت هوا در وضوح زمانی بزرگتر با استفاده از تکنیک های یادگیری عمیق و انتقال یادگیری
منبع:
Sciencedirect - Elsevier - Atmospheric Environment, 214 (2019) 116885: doi:10:1016/j:atmosenv:2019:116885
نویسنده:
Jun Maa, Jack C.P. Chenga, Changqing Lina,b, Yi Tanc, Jingcheng Zhangd,*
چکیده انگلیسی:
As air pollution becomes more and more severe, air quality prediction has become an important approach for air
pollution management and prevention. In recent years, a number of methods have been proposed to predict air
quality, such as deterministic methods, statistical methods as well as machine learning methods. However, these
methods have some limitations. Deterministic methods require expensive computations and specific knowledge
for parameter identification, while the forecasting performance of statistical methods is limited due to the linear
assumption and the multicollinearity problem. Most of the machine learning methods, on the other hand, cannot
capture the time series patterns or learn from the long-term dependencies of air pollutant concentrations.
Furthermore, there is a lack of methods that could generate high prediction accuracy for air quality forecasting
at larger temporal resolutions, such as daily and weekly or even monthly. This paper, therefore, proposes a deep
learning-based method namely transferred bi-directional long short-term memory (TL-BLSTM) model for air
quality prediction. The methodology framework utilizes the bi-directional LSTM model to learn from the longterm
dependencies of PM2.5, and applies transfer learning to transfer the knowledge learned from smaller temporal
resolutions to larger temporal resolutions. A case study is conducted in Guangdong, China to test the
proposed methodology framework. The performance of the framework is compared with other commonly seen
machine learning algorithms, and the results show that the proposed TL-BLSTM model has smaller errors,
especially for larger temporal resolutions
Keywords: Air quality prediction | Large temporal resolution | Deep learning | Long short-term memory | Transfer learning
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0