PROGRESS IN GEOGRAPHY ›› 2019, Vol. 38 ›› Issue (6): 851-860.doi: 10.18306/dlkxjz.2019.06.006

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Urban metabolism prediction of Beijing City based on long short-term memory neural network

Bingchun LIU1(), Xin QI1, Qingshan WANG2,*()   

  1. 1. School of Management, Tianjin University of Technology, Tianjin 300384, China
    2. Tianjin Agricultural University, Tianjin 300384, China
  • Received:2018-10-04 Revised:2019-03-18 Online:2019-06-28 Published:2019-06-27
  • Contact: Qingshan WANG;
  • Supported by:
    National Natural Science Foundation of China, No. 71503180;Major Social Science Projects of Tianjin Education Commission, No. 2017JWZD16.


The underlying causes of aggravating urban environmental pollution, escalating energy consumption, population overcrowding, and other urban environmental problems are imbalances in urban metabolism. In order to accurately predict the trend of urban metabolism changes in Beijing City, the exosomatic metabolic rate of Beijing from 1980 to 2016 was estimated by the indicators of energy consumption and human activity time, and the degree of urban metabolism was characterized. Based on the results, the long short-term memory (LSTM) neural network model was used to predict the exosomatic metabolic rate of various sectors in Beijing from 2017 to 2022. The results show that: 1) the urban metabolic prediction model based on LSTM neural network has high accuracy and can make more accurate prediction on the exosomatic metabolic rate of various sectors in Beijing. 2) From 2017 to 2022, the exosomatic metabolic rate of the primary industry and the overall external energy in Beijing show a downward trend, among which the primary industry reached its peak in 2017, and the exosomatic metabolism rates of the secondary and tertiary industries show an increasing trend. 3) Except for the primary industry, tertiary industry and the overall exosomatic metabolic rate, the temporal perturbation of historical change ranged from small to large. 4) The factors that contribute the most to EMRT are EMR2, and the least are EMR1. This study may provide a theoretical basis and decision-making support for policymakers to optimize urban management plans and enhance urban comprehensive strength.

Key words: long short-term memory neural network, exosomatic metabolic rate, urban metabolism, Beijing