北京城市代谢预测研究——基于长短期记忆神经网络模型

doi:10.18306/dlkxjz.2019.06.006

Abstract

Abstract:

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 EMR_T 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

Bingchun LIU, Xin QI, Qingshan WANG. Urban metabolism prediction of Beijing City based on long short-term memory neural network[J].PROGRESS IN GEOGRAPHY, 2019, 38(6): 851-860.

Figures/Tables 10

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References 40

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年份	第一产业		第二产业		第三产业		生活部门			总体
年份	EMR₁/(MJ/h)	增速/%	EMR₂/(MJ/h)	增速/%	EMR₃/(MJ/h)	增速/%	EMR₄/(MJ/h)	增速/%		EMR_T/(MJ/h)	增速/%
2017	22.36	1.09	143.31	1.3	52.04	2.08	2.89		2.62	10.29	-4.94
2018	22.11	-1.1	145.26	1.36	52.8	1.46	2.99		3.16	10	-2.77
2019	22.08	-0.15	147.23	1.36	53.63	1.57	3.09		3.37	9.55	-4.5
2020	21.95	-0.61	149.23	1.36	54.47	1.56	3.21		4.02	9.16	-4.13
2021	21.86	-0.41	151.26	1.36	55.33	1.57	3.35		4.26	8.69	-5.08
2022	21.75	-0.51	153.32	1.36	56.2	1.59	3.51		4.94	8.21	-5.47

代谢率	情景分类	2018年	2019年	2020年	2021年	2022年
EMR_T	历史情景	10.00	9.55	9.16	8.69	8.21
	低增长率	9.10	8.76	8.22	7.74	7.18
	高增长率	10.90	10.33	10.07	9.61	9.23
EMR₁	历史情景	22.11	22.08	21.95	21.86	21.75
	低增长率	20.29	20.43	20.14	20.05	19.86
	高增长率	23.88	23.71	23.72	23.65	23.61
EMR₂	历史情景	145.26	147.23	149.23	151.26	153.32
	低增长率	131.48	133.26	135.06	136.89	138.74
	高增长率	159.05	161.22	163.43	165.67	167.94
EMR₃	历史情景	52.80	53.63	54.47	55.33	56.20
	低增长率	48.20	48.98	49.72	50.49	51.27
	高增长率	57.38	58.30	59.28	60.29	61.34
EMR₄	历史情景	2.99	3.09	3.21	3.35	3.51
	低增长率	2.73	2.86	2.91	3.02	3.12
	高增长率	3.26	3.33	3.54	3.71	3.97

代谢率	情景分类	2018年	2019年	2020年	2021年	2022年	代数平均值
EMR_T	低增长率	-9.02	-8.27	-10.22	-10.90	-12.57	-10.19
EMR_T	高增长率	9.02	8.14	9.95	10.56	12.31	10.00
EMR₁	低增长率	-8.27	-7.48	-8.22	-8.25	-8.69	-8.18
EMR₁	高增长率	8.01	7.36	8.09	8.19	8.58	8.05
EMR₂	低增长率	-9.49	-9.49	-9.50	-9.50	-9.51	-9.50
EMR₂	高增长率	9.50	9.50	9.51	9.52	9.53	9.51
EMR₃	低增长率	-8.72	-8.68	-8.71	-8.74	-8.78	-8.73
EMR₃	高增长率	8.67	8.71	8.84	8.98	9.14	8.87
EMR₄	低增长率	-8.70	-7.20	-9.45	-9.65	-11.29	-9.26
EMR₄	高增长率	9.20	7.85	10.27	10.82	13.09	10.25

代谢率	EMR₁	EMR₂	EMR₃	EMR₄
EMR₁	0.7977
EMR₂	0.9559	0.8948
EMR₃	0.9531	0.9607	0.8209
EMR₄	0.9202	0.9862	0.9397	0.8464

模型	EMR_T	EMR₁	EMR₂	EMR₃	EMR₄
KPCA-SVR	0.0616	0.1636	0.1048	0.4116	0.2650
KPCA-REG	0.0484	0.1879	0.1321	0.3079	6.0582
KPCA-LAS	0.0883	0.2021	0.1321	0.3081	0.4219
KPCA-GBR	0.0864	0.1133	0.2592	0.4692	0.3193
LSMT	0.0470	0.0476	0.0779	0.0208	0.0585

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

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