基于神经网络综合建模的区域城市群发展脆弱性评价——以滇中城市群为例

doi:10.31497/zrzyxb.20201205

摘要/Abstract

摘要：

城市脆弱性是评价城市发展韧性状况的有效测度。目前我国对于城市脆弱性的研究以运用统计学方法评价特殊城市为主,尚未形成对区域城市群具有普遍适用性的科学客观评价方法。以滇中城市群为例,从环境系统、经济系统、社会系统三个方面综合构建城市脆弱性评价体系,采用熵值法和BP神经网络综合建模方法,对2007—2016年10年间滇中城市群的城市脆弱性进行评价。结果表明：滇中城市群的城市脆弱性总体呈现下降趋势,但城市组团之间差异较大,呈现出发展中的不均衡性。评价结果对滇中城市群韧性发展规划具有重要指导意义,为区域城市群发展脆弱性研究提供一种科学评价方法。

关键词: 城市韧性, 城市脆弱性, 复合系统, BP神经网络, 熵值法, 滇中城市群

Abstract:

Urban vulnerability is an effective measure to evaluate urban development resilience. At present, most studies on urban vulnerability focus on the special city and use the statistical method in China, and there is no urban vulnerability assessment method that is applicable to all the regional urban agglomerations. Taking the Central Yunnan Urban Agglomeration as an example, this paper develops an assessment system of urban vulnerability from three aspects of environmental system, economic system and social system. Entropy method and back propagation neural network model are used to evaluate the urban vulnerability of the study area from 2007 to 2016. The evaluation shows that the vulnerability of this urban agglomeration has a declining trend on the whole, but there is a big difference between the urban groups, which shows an unbalanced development. The evaluation results have reference significance for the planning and resilience development of the Central Yunnan Urban Agglomeration, and provide a scientific evaluation method for the study on the comprehensive development vulnerability of urban agglomeration.

Key words: urban resilience, urban vulnerability, complex system, back propagation neural network, entropy method, Central Yunnan Urban Agglomeration

范峻恺, 徐建刚. 基于神经网络综合建模的区域城市群发展脆弱性评价——以滇中城市群为例[J]. 自然资源学报, 2020, 35(12): 2875-2887.

FAN Jun-kai, XU Jian-gang. Vulnerability assessment of urban agglomeration based on neural network model: A case study of Central Yunnan Urban Agglomeration[J]. JOURNAL OF NATURAL RESOURCES, 2020, 35(12): 2875-2887.

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二级指标	三级指标	单位	指标属性
环境系统	X₁：工业废气排放强度	10⁴t/km²	正向
	X₂：人均年供水量	t/人	负向
	X₃：工业废水重复利用率	%	负向
	X₄：工业固体废物综合利用率	%	负向
	X₅：建成区绿化覆盖率	%	负向
经济系统	X₆：外贸依存度	%	正向
	X₇：实际利用外资占GDP比例	%	正向
	X₈：第三产业占GDP比例	%	负向
	X₉：地区GDP增长率	%	负向
	X₁₀：工业总产值	10⁴元	负向
	X₁₁：固定资产投资强度	10⁴元/km²	负向
社会系统	X₁₂：人口自然增长率	‰	正向
	X₁₃：乡村人口比例	%	正向
	X₁₄：城镇居民人均可支配收入	元	负向
	X₁₅：农民人均纯收入	元	负向
	X₁₆：第三产业就业人数比例	%	负向
	X₁₇：公路通车里程	km	负向

年份	居民消费价格指数	城市居民消费价格指数	农村居民消费价格指数	固定资产投资价格指数
2006	100.0	100.0	100.0	100.0
2007	105.9	105.9	105.9	104.2
2008	111.9	111.6	112.3	111.9
2009	112.4	112.2	112.5	109.8
2010	116.5	116.4	116.5	112.7
2011	122.3	122.0	122.2	117.9
2012	125.6	125.7	125.0	119.6
2013	129.4	130.0	128.4	120.9
2014	132.6	133.3	130.9	122.1
2015	135.1	136.3	132.6	121.0
2016	137.1	138.2	134.8	121.1

二级指标	权重	三级指标	权重
环境系统	0.3025	X₁：工业废气排放强度	0.0825
		X₂：人均年供水量	0.0392
		X₃：工业废水重复利用率	0.1105
		X₄：工业固体废物综合利用率	0.0408
		X₅：建成区绿化覆盖率	0.0295
经济系统	0.5247	X₆：外贸依存度	0.1349
		X₇：实际利用外资占GDP比例	0.1285
		X₈：第三产业占GDP比例	0.0689
		X₉：地区GDP增长率	0.0857
		X₁₀：工业总产值	0.0410
		X₁₁：固定资产投资强度	0.0557
社会系统	0.1728	X₁₂：人口自然增长率	0.0467
		X₁₃：乡村人口比例	0.0372
		X₁₄：城镇居民人均可支配收入	0.0187
		X₁₅：农民人均纯收入	0.0160
		X₁₆：第三产业就业人数比例	0.0218
		X₁₇：公路通车里程	0.0323

等级		I	II	III	IV
环境系统	X₁：工业废气排放强度	0.1385	0.3764	0.6267	1.0000
	X₂：人均年供水量	0.1228	0.4324	0.6198	1.0000
	X₃：工业废水重复利用率	0.0380	0.1170	0.5304	1.0000
	X₄：工业固体废物综合利用率	0.2453	0.5583	0.7566	1.0000
	X₅：建成区绿化覆盖率	0.2090	0.3652	0.7103	1.0000
经济系统	X₆：外贸依存度	0.0758	0.1911	0.6035	1.0000
	X₇：实际利用外资占GDP比例	0.0597	0.1817	0.6248	1.0000
	X₈：第三产业占GDP比例	0.3169	0.6224	0.7646	1.0000
	X₉：地区GDP增长率	0.1491	0.3746	0.6108	1.0000
	X₁₀：工业总产值	0.2229	0.6411	0.8430	1.0000
	X₁₁：固定资产投资强度	0.2550	0.6857	0.8968	1.0000
社会系统	X₁₂：人口自然增长率	0.2933	0.5631	0.7361	1.0000
	X₁₃：乡村人口比例	0.2801	0.6984	0.8449	1.0000
	X₁₄：城镇居民人均可支配收入	0.3525	0.6195	0.8007	1.0000
	X₁₅：农民人均纯收入	0.2579	0.5555	0.7924	1.0000
	X₁₆：第三产业就业人数比例	0.3218	0.5574	0.7420	1.0000
	X₁₇：公路通车里程	0.3329	0.6185	0.8293	1.0000

年份	昆明	曲靖	玉溪	楚雄	红河
2007	9.90	14.41	12.88	10.13	12.62
2008	8.10	14.56	13.72	14.18	13.31
2009	14.00	10.15	7.67	11.95	8.54
2010	11.28	11.34	10.20	13.47	11.83
2011	12.83	14.70	13.47	13.65	14.42
2012	16.83	12.68	11.10	15.03	12.94
2013	10.01	9.72	6.91	7.62	10.01
2014	6.17	-4.53	4.94	8.95	7.18
2015	4.88	3.32	3.09	6.11	6.32
2016	6.77	6.87	3.85	9.34	7.62