دانلود مقاله انگلیسی رایگان:بهبود دقت پیش بینی کیفیت هوا در وضوح زمانی بزرگتر با استفاده از تکنیک های یادگیری عمیق و انتقال یادگیری - 2019
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  • Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques
    Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 10
    حجم فایل: 1101 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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