KOPRI Repository

Prediction of Arctic Sea Ice Concentration Using aFully Data Driven Deep Neural Network

Cited 0 time in scopus
Metadata Downloads
Title
Prediction of Arctic Sea Ice Concentration Using aFully Data Driven Deep Neural Network
Other Titles
위성영상 기반 딥러닝을 이용한 북극 해빙농도 예측
Authors
Chi, Junhwa
Kim, Hyun-cheol
Keywords
Arctic sea ice; autoregressive model; deep learning; global warming; long and short-term memory; machine learning; multilayer perceptron; neural network; sea ice concentration; sea ice extent; Remote Sensing
Issue Date
2017
Citation
Chi, Junhwa, Kim, Hyun-cheol. 2017. "Prediction of Arctic Sea Ice Concentration Using aFully Data Driven Deep Neural Network". REMOTE SENSING, 9(12): 1305-NaN.
Abstract
The Arctic sea ice is an important indicator of the progress of global warming and climate change. Prediction of Arctic sea ice concentration has been investigated by many disciplines and predictions have been made using a variety of methods. Deep learning (DL) using large training datasets, also known as deep neural network, is a fast-growing area in machine learning that promises improved results when compared to traditional neural network methods. Arctic sea ice data, gathered since 1978 by passive microwave sensors, may be an appropriate input for training DL models. In this study, a large Arctic sea ice dataset was employed to train a deep neural network and this was then used to predict Arctic sea ice concentration, without incorporating any physical data. We compared the results of our methods quantitatively and qualitatively to results obtained using a traditional autoregressive (AR) model, and to a compilation of results from the Sea Ice Prediction Network, collected using a diverse set of approaches. Our DL-based prediction methods outperformed the AR model and yielded results comparable to those obtained with other models.
URI
http://repository.kopri.re.kr/handle/201206/6520
DOI
http://dx.doi.org/10.3390/rs9121305
Files in This Item
General Conditions
      ROMEO Green
    Can archive pre-print and post-print or publisher's version/PDF
      ROMEO Blue
    Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
      ROMEO Yellow
    Can archive pre-print (ie pre-refereeing)
      ROMEO White
    Archiving not formally supported

    qrcode

    Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

    Browse