结合网络文本的模糊层次分析法评价水环境敏感性

doi:10.12082/dqxxkx.2019.190165

摘要/Abstract

摘要：

水环境监测数据在水环境敏感性评价中起关键作用,然而受地形、环境、站点布设等因素影响存在缺失或不足的问题。为此,本文选取福建省为实验区,以2017年4-6月该省重点平台采集的网络文本数据为数据源,从水环境网络文本敏感度、污染敏感度和保护敏感度3个方面分析遴选出13个评价因子,基于模糊层次分析法结合网络文本构建水环境敏感性评价模型,分类验证评估结果的合理性。结果表明：① 从网络文本敏感度看,该省东部-中北部地区高于西部-中南部地区,高敏感区分布集中在闽江下游地区;② 从污染敏感度看,该省南部地区高于北部地区,高敏感区主要分布在汀江中下游、晋江下游和龙江等区域;③ 从保护敏感度看,该省西北-中南地区高于东北-西南地区,高敏感区主要分布在闽江上游支流建溪、木兰溪、萩芦溪等区域。综合各敏感度因子分析后发现全省水环境敏感度整体从东南-西南-北部-中部-东北依次减小,东沿海经济发达区和河流入海口等区域呈现出高敏感性,与实际情况相符。本研究使得水环境敏感性评价结果更具合理性,对于预测或排查水环境高敏感污染风险区、重要保护区及公众关注区具有一定的实用意义。

关键词: 水环境, 网络文本, 敏感性评价, 敏感影响因子, 模糊层次分析法, 福建省

Abstract:

Water environment monitoring data plays a key role in water environmental sensitivity assessment. However, insufficiencies remains due to influences of factors such as terrain, environment and site layout. Therefore, this paper took Fujian Province as the experimental area, and used web text collected from key platforms of Fujian Province from April to June 2017 as the data source. Thirteen evaluation factors were selected from three aspects: web text sensitivity, pollution sensitivity and the protection sensitivity of the water environment. Based on the fuzzy analytic hierarchy process combined with the web text, a water environmental sensitivity evaluation model was constructed, and classification validated the rationality of the evaluation results. Results show: (1) In terms of web text sensitivity, the eastern and central-northern region of the province was higher than the western and central-southern region, and the highly sensitive areas were concentrated in Gulou District, Jin'an District and Minhou County in the lower reaches of the Min River. (2) In terms of pollution sensitivity, the southern part of the province (especially the southwest and southeast) was higher than the northern, and the highly sensitive areas were mainly distributed in the middle and lower reaches of Ting River, the estuary of the Jiulong River, and the lower reaches of Jin River and Long River. (3) In terms of protection sensitivity, the northwest-central-south part of the province was higher than the northeast-southwest, and the highly sensitive areas were mainly located in Jiangle County and Mingxi County in the upper reaches of Min River, and Wuyishan City in Jian River, Xianyou County in Mulan River and Hanjiang District in Qiulu River. (4) The water environmental sensitivity of the whole province decreased by the order of the southeast, southwest, north, central, north, and the east, and the economically developed areas along the eastern coast and estuaries of rivers showed high sensitivity, especially in Min River Estuary and Jin River Estuary. (5) The water environmental sensitivity assessment combined with the web text was more accurate in the investigation of highly water environment sensitive areas with pollution risks. Our findings suggest that the proposed method is more reasonable for water environmental sensitivity assessment, and has practical significance for predicting or troubleshooting highly-sensitive pollution-risk areas, important protected areas, and areas of public concern.

Key words: water environment, web text, sensitivity evaluation, sensitive impact factors, fuzzy analytic hierarchy process, Fujian Province

李想, 肖桂荣, 蔡圣准. 结合网络文本的模糊层次分析法评价水环境敏感性[J]. 地球信息科学学报, 2019, 21(12): 1832-1844.DOI:10.12082/dqxxkx.2019.190165

LI Xiang, XIAO Guirong, CAI Shengzhun. Evaluation of Water Environmental Sensitivity based on Fuzzy Analytic Hierarchy Process Combined with Web Text[J]. Journal of Geo-information Science, 2019, 21(12): 1832-1844.DOI:10.12082/dqxxkx.2019.190165

图/表 17

表1

水环境敏感性评价指标"

一类指标	二类指标	影响因子说明	计算方法
网络文本敏感度	特定词频因子	反映关键词中含有特定描述词的数量	$SWS g = n - m$
	重要保护因子	反映关键词中是否包含重点保护对象信息	$PWS g = ∑ k = 1 n 1 + (S k g × λ k) - K min K max$
	文本相关性因子	反映网络文本之间相关性	$RWS g = Max S i (g) S (g)$
	文本数量因子	反映网络文本的数量	$AWS g = n$
污染敏感度	畜禽养殖因子	反映规模化畜禽养殖造成的水环境污染状况	$APS g = ∑ i = 1 n (S i (g) × λ i (g))$
	农田种植因子	反映农田种植造成的水环境污染状况
	水产养殖因子	反映水产养殖造成的水环境污染状况
	城乡生活因子	反映人为消费活动造成的水环境污染状况	$DPS g = ∑ j = 1 n (S j (g) × λ j (g))$
	水质监测指标因子	反映水质监测状况	$MPS g = ∑ i = 1 n w i (g) n$
保护敏感度	河流水域覆盖度因子	反映区域河流水域覆盖情况	$RCS g = S' (g) S (g)$
	自然生态保护因子	反映区域内自然生态保护状况	$PCS g = ∑ k = 1 n 1 + (S k g × λ k) - K min K max$
	饮用水源地保护因子	反映区域内饮用水源地保护状况
	人口密度因子	反映区域内社会环境活动主体的密集性	$DCS g = P (g) S (g)$

表1

图1

图2

图3

表2

图4

图5

表3

图6

表4

图7

表5

图8

表6

图9

表7

图10

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标度	说明
0.50	元素i比元素j同等重要
0.60	元素i比元素j稍微重要
0.70	元素i比元素j明显重要
0.80	元素i比元素j强烈重要
0.90	元素i比元素j极端重要
互补数	若i比j的标注值为b,那么j比i的标注值为1~b

影响因子	B1	B2	B3	权重
影响因子	0.2666	0. 3667	0.3667	权重
C1	0.2000			0.0534
C2	0.2667			0.0711
C3	0.3333			0.0889
C4	0.2000			0.0534
C5		0.1500		0.0550
C6		0.1500		0.0550
C7		0.2000		0.0733
C8		0.1500		0.0550
C9		0.3500		0.1283
C10			0.1833	0.0672
C11			0.3167	0.1161
C12			0.3167	0.1161
C13			0.1833	0.0672

敏感类型	面积/km²	面积占比/%	区县个数	平均贡献率/%	最小贡献率/%	典型区县
特定词频敏感型	26 056.76	19.28	16	58.84	44.20	泰宁县、沙县
重要保护敏感型	61 909.91	45.82	40	54.78	40.43	武夷山市、漳平市
文本数量敏感型	3244.40	2.40	2	64.68	33.32	南安市、闽侯县
文本相关性敏感型	5431.81	4.02	4	48.04	42.93	台江区、鼓楼区
混合敏感型	11 312.18	8.37	8	-	-	龙海市、晋江市
无敏感型	27 161.18	20.10	14	-	-	柘荣县、寿宁县

敏感类型	面积/km²	面积占比/%	区县数/个	平均贡献率/%	最小贡献率/%	典型的区县
城乡生活敏感型	4166.21	3.08	16	78.62	43.46	鼓楼区、湖里区
畜禽养殖敏感型	20 865.60	15.44	11	51.18	42.53	光泽县、永定区
农田种植敏感型	63 354.49	46.89	26	64.68	50.56	松溪县、建瓯市
水产养殖敏感型	5562.96	4.12	7	48.04	30.97	东山县、连江县
混合敏感型	41 166.99	30.47	24	-	-	蕉城区、泉港区

敏感类型	面积/km²	面积占比/%	区县数/个	平均贡献率/%	最小贡献率/%	典型区县
河流水域覆盖度敏感型	13 450.08	9.95	9	67.42	52.15	闽侯县、马尾区
人口密度敏感型	1468.74	1.09	9	82.73	50.50	石狮市、海沧区
饮用水源地保护敏感型	31 042.91	22.97	20	73.43	53.56	华安县、城厢区
自然生态保护敏感型	70 214.18	51.97	33	79.67	54.75	明溪县、武夷山市
混合敏感型	18 940.33	14.02	13	-	-	晋安区、永春县