Advances in Civil Engineering
Volume 2020 (2020), Article ID 8887487, 13 pages
https://doi.org/10.1155/2020/8887487

Research Article

Dynamic Vulnerability Analysis of Mountain Settlements Exposed to Geological Hazards: A Case Study of the Upper Min River, China

Mingtao Ding,1,2 Chuan Tang,² Tao Huang,¹ and Zemin Gao¹

¹Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
²State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China

Correspondence should be addressed to Mingtao Ding; mingtaoding@163.com and Chuan Tang; 2401634274@qq.com

Received 30 May 2020; Revised 15 July 2020; Accepted 7 August 2020; Published 28 August 2020

Academic Editor: Chong Xu

Copyright © 2020 Mingtao Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The upper reaches of Min River (The upper Min River) is located in Southwest China with significant mountain settlements, which are vulnerable to frequent geological hazards. Based on a field investigation, collation of yearbook data, and analysis through the use of SPSS statistical software, a vulnerability evaluation index system of geological hazards was devised. According to the actual field situation and the acquired data of the study area in 2006, 2009, and 2015, 16 indicators were selected as settlement vulnerability evaluation indexes of geological hazards. The indexes included population density, building coverage, and economic density. Based on the comprehensive evaluation model of entropy value, the dynamic change in the settlement vulnerability of geological hazards was analyzed. The results showed that population density, building coverage, economic density, and road density were the factors that affected the settlement vulnerability of geological hazards the most—Wenchuan earthquake caused considerable damage to the upper Min River, making the area the most vulnerable in 2009. However, its vulnerability decreased in 2015, which indicated that postearthquake reconstruction achieved significant results. Thus, the vulnerability has emerged as an important indicator reflecting the safety and healthy development of mountain settlements.

1. Introduction

The vulnerability assessment of geological hazards is a necessary process of regional risk analysis. Furthermore, the vulnerability of geological hazards fluctuates with time. The upper Min River, as a topographic uplift and economic valley area, is in the process of rapid construction of social structure after the settlement was devastated by the massive Wenchuan earthquake of 2008 [1]. Therefore, the dynamic assessment of mountain settlement vulnerability of geological hazards in the upper Min River is an essential part of the mechanism for regional disaster risk reduction, prevention, and capacity building for emergencies.

In 1848, John Stuart Mill pointed out in his book, the Principles of political economy with some of their applications to social philosophy, that all signs of destruction caused by earthquakes, floods, hurricanes, and wars would disappear in a short time and that the country would recover rapidly from the state of disasters, indicating that human and social factors were important aspects affecting disaster risk [2, 3]. As a result, it was gradually realized that improving social disaster-bearing and emergency response capacity was more accessible than changing disaster-causing factors which further helped people understand the importance of vulnerability to disaster research. For natural disasters, the important thing is not to explain the characteristics, consequences, and causes of disasters by the conditions or the actions of disasters but to analyze the contemporary social order, assess the daily relations of disasters, and shape the historical environment of these characteristics [4]. In addition, socioeconomic factors, such as population characteristics, industrial structure, and spatial distribution, have an impact on natural disasters. Therefore, vulnerability is not only a central concept in the field of disaster research but also a means to solve the problems related to population, development, and environment [5, 6].

At present, vulnerability research has developed rapidly—not only as qualitative research but also as quantitative research. Guo [7] analyzed the relationship between natural disasters and social vulnerability, believing that vulnerability includes material and spiritual aspects and that there is a close relationship between social vulnerability and disaster types, disaster nature, disaster intensity, and disaster occurrence time. Hu et al. [8] analyzed the impact of social factors on disasters, pointing out that social factors mainly affect disasters by influencing the source of disasters, the ability of disaster-bearing bodies to prevent and resist disasters, and the level of social and economic development. Liu et al. [9, 10] divided vulnerability evaluation factors into property index and population index. He put forward the conversion assignment function, which solved the problem of unified scaling and comprehensive expression of human, financial, and material aspects and discussed the vulnerability calculation method. Pang et al. [11] analyzed five parts of dynamic risk assessment of natural disasters: time constraints, disaster-causing factors, disaster-bearing bodies, coupling, and scenario output. They also gave a basic model of dynamic risk assessment of natural disasters in a narrow sense.

Meanwhile, many scholars have made vulnerability analyses based on a large number of cases. Taking Kangding County as an example, Wang et al. [12] put forward a new method for evaluating the socioeconomic vulnerability of urban geological hazards and established a vulnerability evaluation system. Tang [13] selected population density, housing property, GDP, and cultivated land and highway distribution as vulnerability evaluation factors of landslide disasters in compiling the landslide risk map of the Red River Basin. Wu et al. [14] selected housing construction, road engineering, lifeline engineering, population distribution, and land use as vulnerability assessment factors in landslide hazard risk assessment in the new urban area of Badong County. Miao and Ding [15] selected eight factors as evaluation indexes. The factors were population density, building coverage, road density, number of hospitals per 10,000 persons, number of welfare institutes per 10,000 persons, number of village committees per 10,000 persons, mobile users' proportion at the end of the year, and number of schools per 10,000 persons. They used the entropy method to obtain the vulnerability zoning map of the Lushan earthquake area.

Vulnerability research has become an indispensable part of natural disaster risk research [16]. However, the overall research still relates mainly to the analysis of the natural factors causing disasters, and there is still a lack of correct understanding and in-depth discussion of the social factors involved in disasters.

This study chooses the mountain settlements in the upper Min River as the research object. It uses the entropy method to carry out the dynamic assessment of the vulnerability of mountain settlements to geological hazards under time constraints and obtains the zoning maps of the vulnerability in different periods. Thus, the study provides an essential scientific basis for the compilation of data on regional geological hazards, planning risk prevention, and the formulation of emergency measures.

2. Study Area

The upper Min River is situated between 30°45′N to 33°10′N and 102°35′E to 103°57′E. This area belongs to the uplift area of China’s topography and the active belt of the tectonic movement. The occurrence of several strong earthquakes in the region has resulted in the loosening of the surface material, damage to deep rocks and the soil mass, and the decrease in water saturation rate in the study area [17, 18]. All these factors have led to frequent secondary disasters in the area, such as topples, slides, and flows. The paper mainly studies the dynamic vulnerability analysis of mountain settlements and does not involve unmanned areas. Therefore, it only selects the geological disaster points that bring harm to human survival or damage the human living environment. The geoenvironmental information system of Sichuan Province [19] stores such data and does not involve geological phenomena such as slides and flows in unmanned areas. According to the geoenvironmental information system of Sichuan province, there have been 2,778 geological hazards in the study area. According to the new classification system proposed by Hunger et al. [20], these geological hazards include 803 slides, 726 topples, 770 flows, and 479 slopes deformation (Figure 1). The study area is inhabited by 22 ethnic groups, such as Tibet, Qiang, Hui, and Han.

Figure 1: Location of the upper Min River (inset map shows Sichuan and China) and the distribution of geological disasters in the study area.

Moreover, the area is home to the largest Qiang ethnic community in China. Its ethnic culture has distinctive characteristics of mixed cohabitation. Besides, under the influence of the farming culture of the Central Plains and the traditional Qiang-Tibetan culture, the region’s economy is still relatively backward [21].

2.1. Distribution and Evolution of Mountain Settlements

The spatial pattern of settlements in the upper Min River has changed significantly from 2006 to 2015. Its distribution is manifested mainly by the migration of the settlements with time from high altitude areas and large slope areas to the river valley platforms. The trend indicated a shift from decentralization to agglomeration. Consequently, the settlement area gradually decreased from 955 km² in 2006 to 462 km² in 2015 (Figure 2).

Figure 2: Distribution of mountain settlements in the upper Min River for different years. (a) 2005. (b) 2009. (c) 2015.

3. Materials and Methods

3.1. Division of Cell Meshes

The primary data of this evaluation are based on the township data, and the operation and processing of the data are determined from the division of regional grid cells. This is done due to the apparent advantage of using grid cells in spatial data superposition calculation. The division of grid size directly affects the rationality of evaluation results. At present, the selection of grid size depends mainly on the resolution of original data and the experience and knowledge of the experts.

“Technical Guide for Evaluation of Resources and Environment Carrying Capacity and Suitability of Land and Space Development” (for trial implementation) [22] proposes to use a grid with a spatial resolution of 50 × 50 m at the regional scale. Therefore, the vulnerability assessment unit of this study adopts a 50 × 50 m resolution grid unit.

3.2. Construction of the Evaluation Index System

The index system of vulnerability assessment includes population, economy, society, and other factors, which referred to the existing research results on disaster vulnerability and other related index systems in mountain areas [23–26]; 43 evaluation indicators were selected from the aspects of population characteristics, social security, social economy, structural characteristics, and natural characteristics. The evaluation index system for the settlement vulnerability of geological hazards in the upper Min River was devised at four levels: target level, criterion level, element layer, and index level (Table 1).

Table 1: Vulnerability evaluation index system of the upper Min River.

Target layer
Criteria layer
Element layer
Index layer
Attribute

Evaluation index of geological disaster vulnerability of mountain settlements
Social population
Disadvantaged
Female ratio
Positive

Number of the disabled
Positive

Number of minimum living security population for urban residents
Positive

The proportion of agricultural and animal husbandry population
Positive

Population ratio under 4 years old and over 65 years old
Positive

Population pressure
Population density
Positive

Building coverage
Positive

Vulnerable occupation
Number of people in the transportation industry
Positive

Number of mining workers
Positive

Construction practitioners
Positive

Socioeconomic
Economic development
Economic density
Positive

Per capita GDP
Negative

Local fiscal revenue
Negative

Savings balance of urban and rural residents
Negative

GDP ratio of the primary industry
Positive

Per capita income of farmers
Negative

Tertiary industry income
Negative

Social capital
Investment attraction assets
Negative

Per capita investment in fixed assets
Negative

General public budget expenditure
Negative

Social security
Social security
Road density
Negative

Number of beds in social welfare institutions
Negative

The proportion of social security expenditure
Negative

Number of village committees per 10,000 persons
Negative

Number of health institutions
Negative

Number of medical technicians per 10,000 persons
Negative

Number of beds per 10,000 persons
Negative

Social culture
Social civilization
Number of people with high school background per 10,000 persons
Negative

Teacher-student ratio
Negative

Radio and television coverage
Negative

Mobile base station coverage rate
Negative

Per capita mobile phones
Negative

Illiteracy ratio
Positive

Customs and culture
The ethnic minority population ratio
Positive

Ethnic culture categories
Positive

Hazard index
Danger of hazard
Hazard density
Positive

Number of people threatened by geological disasters
Positive

Property threatened by geological disasters
Positive

Protective engineering
Protection engineering
Negative

Investment in geological hazard control
Negative

Surface protection
Soil and water loss intensity
Positive

Vegetation coverage
Negative

Topographic undulation
Positive

The bold text represents the final selection indexes.

The vulnerability evaluation indexes of geological hazards are different at different scales. The social and economic development of the upper Min River is reduced, and data collection is complicated. In the selection of indicators, we must not only follow the scientific principle but also fully consider the reality to ensure the availability of data. 27 indexes were excluded because they could not collect the complete time series data. 16 indexes are selected as vulnerability evaluation factors of the geological disasters in the upper Min River. These indexes include property threatened by geological disasters (D1), number of people threatened by geological disasters (D2), population density (D3), hazard density (D4), teacher-student ratio (D5), economic density (D6), GDP ratio of primary industry (D7), building coverage (D8), road density (D9), number of village committees per 10,000 persons (D10), per capita GDP (D11), per capital investment in fixed assets (D12), general public budget expenditure (D13), per capita mobile phones (D14), number of medical technicians per 10,000 persons (D15), and engineering protection (D16).

The basic data of this study are obtained mainly from the following three aspects:

(1)Database on the potential points of geological hazards: According to the geoenvironmental information system of Sichuan province [19] (http://202.61.89.33:16003), information related to coordinates, geographic location, direction, volume, scale, property threatened by geological disasters, number of people threatened by geological disasters, number of households threatened by geological disasters, and prevention engineering of the potential geological hazards in the upper Min River since 2008 was obtained.(2)Remote sensing images and vector data: Based on the National Earth System, science data-sharing infrastructure [27] (http://www.geodata.cn), the enhanced thematic mapper (ETM) image of the study area, and the essential data related to fields like topography, land use, and earthquakes were obtained.(3)Data from yearbooks and statistical bulletins: The data on administrative areas, economic output, a permanent population, practitioners of secondary and tertiary industries, industrial output, built-up areas of towns and townships, and other related data were obtained from several yearbooks and statistical bulletins for the years 2006 to 2016. The sources included “China County Economic Statistical Yearbook,” “Sichuan Statistical Yearbook,” “Aba Statistical Yearbook,” “Chengdu Statistical Yearbook,” “Heishui Statistical Yearbook,” “Wenchuan Statistical Yearbook,” “Mao Statistical Yearbook,” “Songpan Statistical Yearbook,” and “Li Statistical Yearbook.” The data not included in yearbooks and statistical bulletins were obtained through field investigation. For the readjustment of the administrative division of Dujiangyan City, at the end of 2014, Zipingpu Town and Hongkou Town were merged into Longchi Town. However, since statistical data are still divided into the yearbooks for three townships, this article does not make any adjustment to it.

The indices selected for vulnerability assessment were defined as follows:

(1)Property threatened by geological disasters (D1): geological disaster threatening property refers to the amount of property that may be lost after a geological disaster occurs. The more threatening property, the more property that may be damaged, and the higher the vulnerability, which is a positive index.(2)The number of people threatened by geological disasters (D2): the number of people threatened by geological disasters is the number of people threatened by geological disasters in each township. The more people threatened by geological disasters, the more people may be damaged by geological disasters, and the higher the vulnerability, which is a positive index.(3)Population density (D3): population density is the number of people per unit area. Vulnerability is first related to population density. The higher the population density, the higher the vulnerability of settlements, which is a positive index.(4)Hazard density (D4): the more geological disaster points, the greater the potential threat to residents and the greater the vulnerability, which is a positive index.(5)Teacher-student ratio (D5): it is the ratio of students to teachers. The more students assigned by each teacher, the weaker the ability to respond to the crisis. The role of the teacher is very important when disaster strikes. The more teachers are relative to the students in the evacuation and transfer project, the more conducive to ensuring the safety of students’ lives, the higher the antirisk ability, and the lower the vulnerability, which is a negative index.(6)Economic density (D6): economic density refers to the ratio of GDP to the regional area, which characterizes the efficiency of economic activities per unit area of settlements and the intensity of land use. The greater the economic density, the higher the probability of loss in the event of a geological disaster, and the higher the vulnerability of the settlement, which is a positive index.(7)GDP ratio of primary industry (D7): the GDP ratio of primary industry in the upper Min River mainly refers to agriculture, forestry, animal husbandry, and fisheries. The GDP ratio of the primary industry is an industry with more inputs and less income. Such industries were the leading industries in the upper Min River before 2006, reflecting the society. The higher the degree of closure of the structure, the higher the output value ratio, the higher the vulnerability, which is a positive index.(8)Building coverage (D8): buildings are also sensitive to geological disasters and are the main disaster-bearing bodies of geological disasters in the upper reaches of the Minjiang River. The higher the building coverage, the higher the vulnerability of settlements, which is a positive index.(9)Road density (D9): road density refers to the ratio of the length of the road in the area to the area of the area, and it reflects the density of roads. The greater the road density, the higher the probability of damage (89-90), and the higher the vulnerability, which is a positive index.(10)The number of village committees per 10,000 persons (D10): this is the number of village committees owned by every 10,000 people in the study area. Village committees are grassroots mass organizations for self-service, self-management, and self-education of villagers. They are stable and developed in a small area. The primary organization is the grassroots force for the transmission and implementation of national disaster prevention and reduction policies. The more village committees there are, the better the national disaster prevention and mitigation policies are communicated and implemented, and the lower the vulnerability of settlements, which is a negative index.(11)Per capita GDP (D11): per capita GDP is mainly used to assess the economic development level of a region. At present, there are differences between urban and rural areas in my country. In terms of economic conditions, the financial status of rural residents is worse than that of urban residents. And the more barren a place is, the lower the danger awareness of geological disasters, and the higher the degree of damage when a disaster occurs. And the more personal wealth, the stronger the ability to reconstruct after the disaster, which can reduce the vulnerability of the system to a certain extent. Therefore, the higher the per capita GDP of a region, the lower the vulnerability, which is a negative index.(12)Per capita investment in fixed assets (D12): per capita investment in fixed assets is the economic activity of construction and purchase of fixed assets, that is, the reproduction of fixed assets. Investment in fixed assets is the primary means of social fixed asset reproduction. Per capita, fixed assets reflect the strength and ability of disaster reconstruction. The greater the per capita investment in fixed assets, the lower the vulnerability, which is a negative index.(13)General public budget expenditure (D13): it is the expenditure that the region allocates and uses in a planned manner to the centralized budget revenue, and it is the embodiment of the local grassroots construction capacity. The higher the general public budget expenditure, the better the implementation of disaster prevention and mitigation policies, and the lower the vulnerability. It is a negative index.(14)Per capita mobile phones (D14): the number of mobile phones is an essential index of regional communication capabilities. The more the number of telephones per capita, the faster the transmission of disaster information, which is more conducive to benefit and avoid harm, and the lower the vulnerability. It is a negative index.(15)The number of medical technicians per 10,000 persons (D15): it indicates that there are medical technicians for every 10,000 people in the study area, and many places in the mountain regions cannot build a medical system. The number of medical technicians can reflect the local rescue ability. The greater the number of doctors, the better the rescue ability after the disaster and the lower the vulnerability of the settlement, which is a negative index.(16)Engineering protection (D16): protection engineering refers to the number of construction protection projects to control geological disasters. Engineering protection is an effective measure to protect the safety of local residents' lives and property. Therefore, the greater the number of engineering protection, the lower the vulnerability, which is a negative index.

3.3. Evaluation Method

Entropy is an objective, comprehensive evaluation method whose concept originated from thermodynamics [28–31]. It is a measure of uncertainty—the smaller the information entropy of the indicators, the higher the difference coefficient and the weight of the indicators. This means that the information provided by the indicators is large. Thus, it plays an essential role in the comprehensive application. The entropy method is increasingly applied in engineering, management, and social sciences and has gradually become an indispensable practical method in scientific research. Miao and Ding [30] took Lushan and Ludian earthquake-stricken areas as research areas and used the entropy comprehensive evaluation method to determine the social vulnerability of the affected areas. This further proved that the entropy method is practical in social vulnerability assessment. According to the theory and method of information entropy, Ding et al. [32] put forward the concept of “geological hazard entropy” and applied it to the evaluation of geological hazards in the Ranwu-Dongjiu section of Sichuan-Tibet Highway and achieved excellent results.

This study combines the entropy method with geographic information system (GIS) to objectively and intuitively analyze the settlement vulnerability of geological hazards in the upper Min River. The main steps of the entropy comprehensive evaluation method are as follows [31]:

(a)Normalization of indexes [31]: Due to the differences in the dimensions, orders of magnitude, units, and quantity changes of the selected indicators in the study, these differences may have an impact on the final analysis results. In order to eliminate these effects, it is necessary to uniformly eliminate the dimensions of the data to solve the problem of the homogenization of qualitative index values. Moreover, because the values of positive and negative indicators represent different meanings (the greater the value of the positive indicator, the greater the vulnerability of mountain settlements, and the smaller the value of the negative indicator, the greater the vulnerability of mountain settlements) Therefore, we use different algorithms to standardize data for positive and negative indicators. The mountain settlements in the upper Min River are divided into n grids, and p indicators are selected. is the value of the jth indicator of the ith grid unit (i = 1, 2, …, n; j = 1,2, …, p). The matrix is as follows:

 When Xij is a positive indicator [31],

 When Xij is a negative indicator [31],

 where i represents the sample; j represents the indicator, represents the standardized value of the ith sample under the j indicator, represents the minimum value in the j indicator value, and represents the maximum value in the j indicator value. Normalized data after calculation:

(b)Computation of entropy value [31]:

 where ej represents the entropy value of index j; k > 0; represents the weight of scheme i under index j; n represents the number of samples.(c)Computation of the different coefficient [31]:

 where represents the different coefficient of index j and ej is the same as in (5) above.(d)Computation of the index weight [31]:

where aj represents the weight of index j and is the same as in (6) above.

Through ArcGIS software, establish the raster layer of each index, use the above entropy value calculation formula, and use the raster calculator to calculate the vulnerability to obtain the vulnerability value of the mountain settlement. At the same time, in order to conduct a comparative analysis of the vulnerability of mountain settlements in the upper Min River in different periods, the results of mountain settlement evaluations in 2006, 2009, and 2005 were subjected to dispersion standardization (Min-Max standardization). So that the vulnerability index of each period is changed to between [0,1]. The vulnerability level is divided into 5 categories using the equal division method, which are in turn, low (0.0–0.2), comparatively low (0.2–0.4), medium (0.4–0.6), comparatively high (0.6–0.8), and high (0.8–1.0) five mountain settlement vulnerable areas. The standardized transfer function for dispersion is as follows [31]:

where X′ is the normalized value of the sample data, X is the original value of the sample data, max is the maximum value of the sample data, and min is the minimum value of the sample data.

4. Results and Analysis

4.1. Computation and Analysis of Vulnerability Indicators

The weights of various indexes changed greatly in 2006, 2009, and 2015 (Table 2). In 2006, the weights of road density, economic density, building coverage, and population density were larger, and the weight of per capita GDP and general public budget expenditure was lower. In 2009, the weights of building coverage and road density were higher, and the weight of per capita GDP, general public budget expenditure, and number of medical technicians per 10,000 persons were lower. In 2015, the weights of building coverage and highway density were higher, while the weights of general public budget expenditure, per capita mobile phones, and the number of medical technicians per 10,000 persons were lower.

Table 2: Entropy value, difference coefficient, and weight of vulnerability indicators of geological hazards in the upper Min River.

No.
Parameter
2006
2009
2015

ej

aj
ej

aj
ej

aj

D1
Population density
0.7702
0.2298
0.1322
0.8424
0.1576
0.1161
0.8422
0.1578
0.1028

D2
Building coverage
0.7645
0.2355
0.1354
0.7287
0.2713
0.1999
0.6045
0.3955
0.2577

D3
Economic density
0.6607
0.3393
0.1951
0.8590
0.1410
0.1039
0.7773
0.2227
0.1451

D4
Per capita GDP
0.9850
0.0150
0.0086
0.9950
0.0050
0.0037
0.9843
0.0157
0.0102

D5
GDP ratio of primary industry
0.9323
0.0677
0.0389
0.9406
0.0594
0.0438
0.8754
0.1246
0.0812

D6
Per capita investment in fixed assets
0.9513
0.0487
0.0280
0.9189
0.0811
0.0598
0.9818
0.0182
0.0119

D7
General public budget expenditure
0.9964
0.0036
0.0021
0.9946
0.0054
0.0040
0.9963
0.0037
0.0024

D8
Road density
0.6671
0.3329
0.1914
0.7637
0.2363
0.1741
0.7896
0.2104
0.1371

D9
Number of village committees per 10,000 persons
0.9764
0.0236
0.0135
0.9828
0.0172
0.0127
0.9801
0.0199
0.0130

D10
Number of medical technicians per 10,000 persons
0.9680
0.0320
0.0184
0.9901
0.0099
0.0073
0.9935
0.0065
0.0042

D11
Teacher-student ratio
0.9603
0.0397
0.0228
0.9744
0.0256
0.0189
0.9510
0.0490
0.0319

D12
Per capita mobile phone
0.9439
0.0561
0.0323
0.9551
0.0449
0.0331
0.9919
0.0081
0.0053

D13
Hazard density
0.8955
0.1045
0.0601
0.8932
0.1068
0.0787
0.8932
0.1068
0.0696

D14
Number of people threatened by geological disasters
0.8904
0.1096
0.0630
0.9053
0.0947
0.0698
0.9053
0.0947
0.0617

D15
Property threatened by geological disasters
0.9036
0.0964
0.0554
0.9039
0.0961
0.0708
0.9039
0.0961
0.0626

D16
Engineering protection
0.9952
0.0048
0.0028
0.9952
0.0048
0.0036
0.9952
0.0048
0.0031

In the evaluation process using the entropy method, the indicators with too small weights can be excluded according to the size of the entropy weight, in order to facilitate a more accurate and reliable evaluation [27]. According to Table 2, teacher-student ratio, number of village committees per 10,000 persons general public budget expenditure, number of medical technicians per 10,000 persons, and engineering protection with aj < 0.03 should be excluded. The 11 indicators finally selected property threatened by geological disasters (N1), the number of people threatened by geological disasters (N2), population density (N3), hazard density (N4), economic density (N5), GDP ratio of primary industry (N6), building coverage (N7), road density (N8), per capita GDP (N9), per capital investment in fixed assets (N10), and per capita mobile phones (N11). Finally, calculate the weight of each indicator in 2006, 2009, and 2015 again (Table 3).

Table 3: Final weight of vulnerability indicators of geological hazards in the upper Min River.

N1
N2
N3
N4
N5
N6
N7
N8
N9
N10
N11

aj
2006
0.0590
0.0670
0.1405
0.0639
0.2074
0.0414
0.1440
0.2035
0.0092
0.0298
0.0343

2009
0.0743
0.0732
0.1218
0.0825
0.1090
0.0459
0.2096
0.1826
0.0038
0.0627
0.0347

2015
0.0663
0.0653
0.1088
0.0736
0.1535
0.0859
0.2726
0.1450
0.0108
0.0126
0.0056

4.2. Results of Vulnerability Assessment and Its Dynamic Changes

As shown in Table 4, the medium and low vulnerability areas accounted for the highest proportion between 2006 and 2015. The vulnerable areas changed from medium to low vulnerable areas, which indicates that the implementation of risk-aversion relocation and land-change relocation policies changed the vulnerability of the upper Min River. According to the percentage of each vulnerable area, in the highly vulnerable area, the percentage trend of vulnerable areas was 2006 > 2015 > 2009; in the middle and comparatively high vulnerable areas, the trend was 2006 > 2009 > 2015; and in the comparatively low and low vulnerable areas, the trend of the proportion of lower vulnerability was 2015 > 2009 > 2006. The main reason for this variation was the evacuation and relocation projects of the Wenchuan disaster reconstruction plan that made many people move to dangerous areas.

Table 4: Vulnerability zoning results of geological hazards in the upper Min River for 2006, 2009, and 2015.

Vulnerability zoning
2006
2009
2015

Area (km²)
Area (%)
Area (km²)
Area (%)
Area (km²)
Area (%)

High
142.1663
14.88
56.8283
6.98
61.7794
13.35

Comparatively high
101.2278
10.59
169.2009
20.78
31.2595
6.75

Middle
440.9356
46.14
325.557
39.99
72.7635
15.72

Comparatively low
177.2316
18.54
139.7377
17.16
234.8497
50.73

Low
94.1431
9.85
122.7882
15.08
62.2737
13.45

In 2006, most settlements in Wenchuan, Mao, and Heishui counties had a high vulnerability to geological hazards—mainly due to their dense population, frequent economic activities, and more potential hazards. Comparatively, higher and middle vulnerable areas were distributed mainly on both sides of the Min River in Wenchuan, Mao, and Songpan counties. The vulnerability of geological hazards in most areas of Li County was relatively low, and the relatively high vulnerability areas were mainly near Zagunao Town. Wolong Town, and Gengda Town in Wenchuan County which are the natural reserves of the giant panda. Because of better vegetation, less human engineering activities, and sparse distribution of geological hazards and settlements, the vulnerability of geological hazards was relatively low (Figure 3(a)).

Figure 3: Vulnerability zoning of geological hazards in the upper Min River for several years. (a) 2006. (b) 2009. (c) 2015.

In 2009, the areas with the high and comparatively high vulnerability of geological hazards in the upper Min River were concentrated mainly in Songpan County, Heishui County, and townships on both sides of the mainstream of Min River in Mao County. Except for the high and comparatively high vulnerability in Songpan County, the upper reaches of the Heishui River Basin and the villages and towns on the right side of the river course were moderately vulnerable. The vulnerability values of the Zagunao River Basin in Li County and the Yuzixi River Basin in Wenchuan County were low as a whole, while those of the Shouxi River Basin were lower (Figure 3(b)).

In 2015, the settlement vulnerability of geological hazards in the upper Min River was relatively low (Figure 3(c)). Nevertheless, the highly vulnerable areas were mainly Fengyi Town in Mao County, Weizhou Town in Wenchuan County, and Jinan Town and Jinan Hui Township in Songpan County. These areas are mainly the government locations of the districts and counties, which are relatively low on both sides of the river and are conducive to settlement production. However, the upper Min River has deep mountains and valleys, which are prone to geological disasters and have a greater potential for vulnerability. Hongkou Township of Dujiangyan has a comparatively high vulnerability. Due to its large population and dense economy, the potential loss is enormous. Except for the high and comparatively high vulnerability in Songpan County, the upper reaches of the Heishui River Basin and the villages and towns on the right side of the river course were moderately vulnerable. The vulnerability values of the Zagunao River Basin and the Yuzixi River Basin were low as a whole.

5. Discussion

The main indicators of the vulnerability of geological hazards in the upper Min River were population density, building coverage, economic density, and road density. They indicate that population, buildings, and highways are the main disaster-bearing bodies of geological hazards in the upper Min River and that regional economic value is the most direct loss of disaster threat objects. Among the factors contributing less, GDP per capita and general public budget expenditure were relatively low in 2006. The reasons that accounted for this were the mountain areas in the upper Min River, poor economic foundation, lack of revenue sources for the government, low corresponding public budget expenditure, and internal differences. In 2009, per capita GDP, general public budget expenditure, and the number of medical technicians per 10,000 persons accounted for the lowest share. Compared with 2006, after the Wenchuan earthquake, there was an increase in the number of medical technicians per 10,000 persons, showing higher medical and health investment by the government in remote areas. As a result, the regional differences in the distribution of doctors and technicians narrowed. More importantly, although the share of general public budget expenditure was still small, it was quite different from the situation in 2006. The government still invested less in the area in 2006, but the state invested a lot of money after the Wenchuan earthquake, and with support from all parts of the country, the general public budget input in 2009 was very high compared with that in 2006.

Compared with 2006 and 2009, in 2015, the weight of per capita mobile phones was relatively lower. The study area still had mainly fixed phones and functional computers in 2006 and 2009, and the mobile infrastructure was not perfect. With imperfection in mobile infrastructure, there was a big difference in the per capita telephones in each town. In 2015, due to the government’s construction of basic communication facilities in the region, coupled with the popularization of 3G and 4G signals, smartphones entered thousands of mountain families, and now almost every household has a mobile phone.

The average vulnerability of villages and towns in the upper Min River was 0.2213 in 2006, 0.2290 in 2009, and 0.2251 in 2015. The order of the vulnerability value was 2009 > 2015 > 2006, and the overall vulnerability of the upper Min River was low. The main reason for the highest vulnerability value in the area in 2009 was earthquake-stricken areas of Wenchuan, where production and life were seriously affected by earthquakes and the secondary geological hazards caused by the earthquake.

The vulnerability value in 2015 was much lower than that in 2009, but it was still higher than that in 2006, which indicates that the reconstruction effect of the Wenchuan earthquake was remarkable, but there was a need for strengthening disaster prevention and mitigation mechanism. Before 2015, five counties in the upper Min River were deeply poverty-stricken (among them Heishui County was a state-level poverty-stricken county). With the deepening implementation of the precise poverty alleviation policy, the economic development of the region has been rapid in the past 10 years. Especially after 2008, the national postdisaster reconstruction has invested a lot of manpower and material resources in the region, bringing Mao, Wenchuan, and Li counties out of poverty successively in 2017, which is consistent with the results of vulnerability study.

According to the ascending order of vulnerability values of 84 townships in the upper Min River in 2006, 2009, and 2015 (Table 5), the time-varying curve of settlement vulnerability of geological hazards can be obtained (Figure 4). From the trend of change, the vulnerability curve is 76–78 in villages and towns, which divides the whole area into two parts: high value and low-value areas. The vulnerability trend of high value area is 2009 > 2015 > 2006, and that of low value area is 2009 > 2006 > 2015. In each year, the proportion of towns in the low-value areas is higher, while that of the high-value area is lower. Vulnerability rises faster in the high-value area and slower in the low-value area, which is like the “Matthew effect” in economics. This special “Matthew effect” can better reflect the changing trend of vulnerability on a time scale. That is, the settlement vulnerability of geological hazards has polarized—the areas with high vulnerability value continue to rise and the areas with low vulnerability value continue to decrease. This also indirectly indicates that we should focus on building a disaster prevention and emergency response capacity in highly vulnerable areas.

Table 5: Vulnerability of townships to geological hazards in the upper Min River for 2006, 2009, and 2015.

No.
Vulnerability in 2006
Township
Vulnerability in 2009
Township
Vulnerability in 2015
Township

1
0.0000
Jiabi
0.0000
Jiabi
0.0000
Jiabi

2
0.0268
Miyaluo
0.0390
Sanjiang
0.0211
Miyaluo

3
0.0362
Guergou
0.0459
Miyaluo
0.0398
Guergou

4
0.0393
Shangmeng
0.0526
Shangmeng
0.0419
Shangmeng

5
0.0545
Puxi
0.0572
Guergou
0.0488
Puxi

6
0.0623
Putou
0.0597
Gengda
0.0570
Weicheng

7
0.0634
Tonghua
0.0649
Yinxing
0.0610
Songpinggou

8
0.0638
Sanjiang
0.0821
Putou
0.0664
Putou

9
0.0924
Yinxing
0.0829
Weicheng
0.0672
Sanjiang

10
0.0960
Gengda
0.0860
Puxi
0.0721
Tonghua

11
0.0971
Taoping
0.0955
Tonghua
0.0755
Diexi

12
0.1022
Xuecheng
0.1019
Wolong
0.0766
Yinxing

13
0.1042
Xiameng
0.1062
Songpinggou
0.0813
Wolong

14
0.1073
Wolong
0.1102
Diexi
0.0819
Gengda

15
0.1129
Ganbao
0.1163
Taoping
0.0850
Wadi

16
0.1172
Muka
0.1197
Longxi
0.0851
Kalong

17
0.1223
Caopo
0.1218
Xiameng
0.0879
Taoping

18
0.1319
Xiabazhai
0.1305
Ganbao
0.0951
Taiping

19
0.1350
Yanyun
0.1327
Shidaguan
0.0984
Sanlong

20
0.1368
Weicheng
0.1353
Xuecheng
0.0997
Caopo

21
0.1404
Shangbazhai
0.1377
Heihu
0.1007
Muka

22
0.1408
Longxi
0.1396
Wadi
0.1017
Shidaguan

23
0.1439
Hongzha
0.1412
Kalong
0.1017
Xiameng

24
0.1442
Songpinggou
0.1424
Caopo
0.1026
Xuecheng

25
0.1446
Yanmen
0.1424
Sanlong
0.1035
Heihu

26
0.1493
Caoyuan
0.1624
Qugu
0.1040
Shashiduo

27
0.1497
Yingxiu
0.1626
Yadu
0.1075
Ganbao

28
0.1500
Dazhai
0.1626
Shashiduo
0.1171
Longxi

29
0.1502
Shuijing
0.1628
Taiping
0.1185
Yanmen

30
0.1504
Mouni
0.1653
Baixi
0.1198
Luoduo

31
0.1609
Shanba
0.1737
Yanmen
0.1249
Qugu

32
0.1656
Diexi
0.1739
Muka
0.1265
Yadu

33
0.1685
Taiping
0.1764
Luoduo
0.1291
Baixi

34
0.1698
Miansi
0.1800
Ciba
0.1291
Ciba

35
0.1705
Shashiduo
0.1806
Miansi
0.1343
Longba

36
0.1709
Hongkou
0.1861
Longba
0.1407
Huilong

37
0.1712
Daxing
0.1905
Yanyun
0.1412
Yingxiu

38
0.1754
Heihu
0.1962
Shangbazhai
0.1421
Zhimulin

39
0.1783
Hongtu
0.1990
Goukou
0.1423
Zhawo

40
0.1790
Wadi
0.1994
Hongzha
0.1461
Waboliangzi

41
0.1819
Shidaguan
0.1994
Xiabazhai
0.1467
Qinglang

42
0.1823
Sanlong
0.2009
Zhimulin
0.1530
Mawo

43
0.1834
Xiaoxing
0.2023
Waboliangzi
0.1552
Keku

44
0.1888
Kalong
0.2033
Shuijing
0.1576
Goukou

45
0.1890
Mawo
0.2037
Mouni
0.1632
Shidiaolou

46
0.1915
Luoduo
0.2039
Qinglang
0.1764
Shuangliusuo

47
0.1953
Ciba
0.2063
Shidiaolou
0.1828
Weigu

48
0.1964
Zhenping
0.2112
Nanxin
0.1852
Hongyan

49
0.1968
Baixi
0.2114
Zhawo
0.1853
Feihong

50
0.1968
Longba
0.2116
Caoyuan
0.2044
Weimen

51
0.2042
Keku
0.2128
Shanba
0.2193
Musu

52
0.2064
Yadu
0.2148
Dazhai
0.2538
Miansi

53
0.2064
Longchi
0.2267
Huilong
0.2578
Hongkou

54
0.2084
Qinglang
0.2278
Hongtu
0.2672
Xiabazhai

55
0.2131
Chuanzhusi
0.2298
Mawo
0.2710
Longchi

56
0.2149
Shuangliusuo
0.2306
Yingxiu
0.2721
Hongzha

57
0.2162
Zhawo
0.2306
Daxing
0.2721
Yanyun

58
0.2162
Zhimulin
0.2322
Feihong
0.2750
Dazhai

59
0.2180
Minjiang
0.2354
Keku
0.2752
Shangbazhai

60
0.2241
Qugu
0.2401
Xiaoxing
0.2794
Shuijing

61
0.2245
Hongyan
0.2437
Hongyan
0.2817
Mouni

62
0.2299
Shidiaolou
0.2510
Weigu
0.2835
Daxing

63
0.2323
Shili Hui
0.2545
Shuangliusuo
0.2886
Shanba

64
0.2325
Zhenjiangguan
0.2547
Seergu
0.2897
Caoyuan

65
0.2328
Goukou
0.2597
Weimen
0.3040
Hongtu

66
0.2354
Waboliangzi
0.2636
Minjiang
0.3062
Xiaoxing

67
0.2368
Anhong
0.2660
Zhenping
0.3177
Zhenping

68
0.2439
Huilong
0.2769
Chuanzhusi
0.3200
Minjiang

69
0.2546
Qingyun
0.2797
Anhong
0.3293
Nanxin

70
0.2571
Musu
0.2807
Musu
0.3411
Zhenjiangguan

71
0.2716
Weigu
0.2809
Zhenjiangguan
0.3458
Anhong

72
0.2745
Zagunao
0.3028
Shili Hui
0.3467
Chuanzhusi

73
0.2825
Feihong
0.3087
Hongkou
0.3542
Zipingpu

74
0.2825
Seergu
0.3204
Shuimo
0.3609
Qingyun

75
0.3040
Weimen
0.3289
Qingyun
0.3616
Xuankou

76
0.3129
Nanxin
0.3461
Luhua
0.3699
Shili Hui

77
0.3930
Shuimo
0.3475
Longchi
0.3870
Seergu

78
0.5282
Jinan Hui
0.4193
Xuankou
0.3874
Shuimo

79
0.6186
Luhua
0.4935
Zipingpu
0.5389
Luhua

80
0.6978
Jinan
0.5829
Jinan Hui
0.6135
Zagunao

81
0.7608
Fengyi
0.6386
Zagunao
0.8388
Fengyi

82
0.7730
Weizhou
0.8669
Jinan
0.8412
Jinan Hui

83
0.8081
Zipingpu
0.9191
Weizhou
0.9766
Jinan

84
1.0000
Xuankou
1.0000
Fengyi
1.0000
Weizhou

The bold text indicates the transition zone from low‐value vulnerability area to high-value area.

Figure 4: Vulnerability change of geological hazards in the upper Min River.

6. Conclusions

Vulnerability, as a part of risk assessment, is an indispensable part of regional risk control. Wenchuan earthquake, geological hazards, settlement migration, socioeconomic conditions, and ethnic cultural changes have a profound impact on the mountain settlement vulnerability of geological hazards in the upper Min River. The migration of mountain settlements in this area varies greatly. Due to national policy, the area of settlements has been drastically reduced, and the population living in traditional farming and animal husbandry has gradually moved to cities. As main destinations of settlement migration, the county towns of each district and county should strengthen the capacity building of disaster prevention and emergency response.

The contribution share of each index is different in different years. Population density, building coverage, economic density, and road density are the main factors affecting the settlement vulnerability of geological hazards in the upper Min River. Therefore, strengthening the construction of the regional population, economy, and infrastructure is conducive to reducing the vulnerability of the area.

The settlement vulnerability of geological hazards in the upper Min River is witnessing a dynamic change, with the highest vulnerability in 2009, the second in 2015, and the lowest in 2006. Although the Wenchuan earthquake caused considerable damage in the area, significant achievements have also been made in the postearthquake reconstruction. The time fluctuation of vulnerability distribution in villages and towns is consistent. The trend of vulnerability distribution is basically in line with the “Matthew effect.” The areas with high vulnerability to settlement geological hazards will continue to increase. Therefore, we should focus on the prevention of geological hazards, the creation of an investment in reconstruction, and building emergency capacity in highly vulnerable areas.

Data Availability

The vulnerability evaluation data and relative weights used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This research was financially supported by the National Key Research and Development Program (Grant no. 2018YFC1505402), National Natural Science Foundation of China (Grant no. 41871174), the project of Science and Technology Department of Sichuan Province (Grant no. 2020YFSY0013), Opening Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology) (Grant no. SKLGP2019K018), and the Fundamental Research Funds for the Central Universities (Grant no. 2682019CX19).

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