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    <identifier>10.57760/sciencedb.05426</identifier>
    <datestamp>2022-10-26T14:06:03Z</datestamp>
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  <dc:date>2022-10-26</dc:date>
  <dc:title>Intermediate data of the accuracy curves on possibility risk, consequence risk and final risk.</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.05426</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>The data were calculated by the GSS-RiskAseer,&amp;nbsp;we&amp;nbsp;compare our GSS-RiskAsser with the following popular deep-learning models: (1) CNN-based methods. We only apply CNN architecture to process visual information and predict gas supply system risk. The models we compare are ResNet50 and ResNet101. (2) RNN-based methods. In these baselines, we only exploit score matrix by DNN, GRU or LSTM to predict risk value. (3) CNN-RNN fusion methods. We extract multi-modal features by CNN and RNN separately and concatenate them to predict risk.</dc:description>
  <dc:subject>possibility risk ; consequence risk; final risk</dc:subject>
  <dc:creator>li xue fei</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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