Interpretable carbon emission prediction model based on key feature ranking
An interpretable carbon emission prediction model(EEMD-LSTM-ATT)based on the key feature ranking was proposed,where six variables were selected,i.e.the total population,the urbanization rate,the primary industry GDP,the secondary industry GDP,the tertiary industry GDP,and the total import/export trade.Using the long and short-term memory network,which has a strongly nonlinear prediction ability,as the baseline model,innovatively adopts the attention mecha-nism to extract the influencing factors and the total amount of trade.The attention mechanism was used to extract the weight information of the influencing factors and time attributes.The results show that,on one hand,the model can inhibit the gen-eration of modal overlap and reduce the influence of data nonlinearity on the model prediction;on the other hand,it can ex-plain the importance of different time attributes and different influencing factors on the carbon emission,which makes the pre-diction results interpretable.In addition,the weighting information of influencing factors and time attributes is added to the training process of the model,which can promote the organic combination of carbon emission influencing factors and model prediction.The method of this paper can achieve a high-precision carbon emission prediction,with the RMSE being 3.772,the RMAE being 3.416,and the R2 being 0.880.
ensemble empirical mode decompositionlong and short-term memory modelattention mechanismprediction model
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