Eco-Geological Environment Quality Assessment Based on FAHP-CV Combination Weighting

Abstract

This study evaluated the eco-geological environment quality of Hunjiang District, Baishan City, Jilin Province. Fifteen indicators were selected from geological, ecological, and social aspects to make the eco-geological environmental quality assessment more comprehensive. On the basis of improved game theory, two weighting methods of FAHP-CV (Fuzzy Analytic Hierarchy Process and Coefficient of Variation) were used to calculate the weight, and finally ArcGIS was used to intuitively understand the eco-geological environment quality of the study area. According to a threshold value, the eco-geological environment quality of the study area was divided into five types: better, good, medium, bad, and worse, and the results show that the overall eco-geological environment quality of the study area is in the middle and upper levels. The eco-geological environment quality of a small part of the area is poor. The classification of the eco-geological environment quality of Hunjiang District provides a scientific basis for the establishment of reasonable eco-geological environment protection and urban planning in the future.

1. Introduction

The eco-geological environment is an important basic environment for human survival that has a far-reaching impact on the development of human society. At the same time, biological activities affect and change the eco-geological environment [1,2]. With the acceleration of human economization, geological disasters, environmental pollution, and other problems have become increasingly prominent, and the ecological environment has been seriously damaged [3,4]. The large-scale development and transformation of nature has seriously affected the regional ecological environment and geological environment and caused environmental deterioration, and resource shortages and natural disasters occur frequently, which pose a great threat to regional development, social stability, and national security. In this grim situation, how to coordinate the relationship between social development and eco-geological environmental protection to achieve regional sustainable development has become an urgent problem to be solved. Accurate eco-geological environment assessment can improve the eco-geological environment, effectively control human activities, and promote the sustained and healthy development of human beings. It is beneficial for local governments to provide support for the development, transformation, and upgrading of existing industries, as well as a reliable guarantee for the future of green and sustainable development. Therefore, the scientific evaluation of the eco-geological environment is conducive to objectively determining regional geological environmental conditions and possible geological environmental problems [5,6] and is convenient for decision makers to formulate reasonable policies for eco-geological environment protection. in order to promote the sustainable development of human society [7,8]. It also provides a certain scientific basis for the rational utilization of resources and the restoration and management of the ecological environment in the study area.

Eco-geological environmental quality assessment is an important part of environmental quality assessment, which is developing rapidly as a branch of environmental assessment [9]. Since Zhou Aiguo and others published the monograph Theory and Application of Geological Environmental Quality Assessment in 1998 [10], Chinese scholars have carried out a great deal of fruitful work on geological environmental quality assessment. In recent years, with the development of “3s” technology, the application of GIS technology in eco-geological environmental quality assessment has become a hot topic at home and abroad [11,12]. At the same time, the research on geo-environmental quality and eco-geological environmental quality assessment has been diversified in its methods, enabling the research to reach new heights [13]. For example, Xu Hua and others applied AHP to evaluate the geological environment quality of mines in Suizhong County [2]. Chen Chaoliang et al. used AHP to comprehensively evaluate the eco-geological environment quality of Neijiang City and established an eco-geological environment quality information database [14]. Jiao Wenting et al. used the methods of factor analysis and vector evaluation to evaluate the environmental carrying capacity of the Ningxia Hui Autonomous Region. The evaluation results show that its environmental carrying capacity cannot meet the requirements of economic construction for the eco-geological environment in this region [15]. With the help of GIS technology, Wang Zufeng and others studied the influencing factors, mechanism, and law of eco-geological environment vulnerability along Duwen Highway and summarized an evaluation system and method suitable for the mountain geological and ecological environment [16].

This study took Hunjiang District, Baishan City, Jilin Province as its research area. The eco-environmental problems in Hunjiang District mainly arise from the contradiction between human production and life activities and the natural ecological environment. The eco-geological environmental quality assessment of this study involved comprehensively analyzing and studying the basic geology, climate and hydrology, geological disasters, and other basic conditions in the Hunjiang area through field investigation and data collection. We used ArcGIS to divide the evaluation grade of the study area to make the evaluation accurate and comprehensive. This study has important guiding significance for improving and harnessing the eco-geological environment of this area and provides a scientific basis for managing geological disasters and ecological restoration and treatment.

2. Materials and Methods

2.1. Study Region Generalization

Hunjiang District of Baishan City is located in the southeast of Jilin Province, covering an area of 1388 square kilometers, with geographical coordinates from 126°07′ to 126°41′ east and 41°30′ to 42°04′ north. Tonghua County, Tonghua City is to the west, Liuhe County to the north, Ji’an City to the south, Jiangyuan County and Linjiang City to the east, and the Democratic People’s Republic of Korea to the southeast (Figure 1). The border is 45 km long, which is the seat of Baishan City government.

The study area has an obvious continental monsoon climate in the mid-temperate zone, with the alternation of spring and autumn and four distinct seasons, characterized by a mild and short spring, hot and rainy summer, cool and dry autumn, and long and cold winter. Hunjiang District, which belongs to the flora of Changbai Mountain, is one of the key forest areas of Baishan City, which is rich in forest resources. The area of forestry in the region is 110,000 hectares, accounting for 80.44% of the total land area. The annual average temperature is 3 to 5 degrees Celsius, the frost-free period is generally 115 to 140 days, and the average annual rainfall is 800–900 mm, mostly from June to August. Human production activities in Hunjiang District of Baishan City have formed many high, steep, and unstable slopes prone to collapse and geological disasters, which have a serious impact on the production and living environment of local residents. The contradiction between eco-environmental problems and local residents is becoming more and more serious, and the governance of the eco-environment is extremely urgent.

2.2. Selection and Classification of Evaluation Indicators

The eco-geological environment is a huge and complex system composed of many parts which are relatively independent and interrelated. Reasonable evaluation indicators are very important for the evaluation results [17]. In this study, 15 indexes were selected from 3 standard layers of the geological environment, ecological environment, and social environment, and then, based on FAHP [18,19], the coefficient of variation method [20], improved game theory, GIS, and field investigation, the study area was evaluated and analyzed. The overall research framework is shown in Figure 2.

2.2.1. Principles for Selecting Indicators

Eco-geological environmental quality assessment is a complex process related to ecological, geological, and environmental conditions [21]. Therefore, it is very important to select scientific and reasonable evaluation indexes when evaluating the quality of the eco-geological environment. According to the construction principles of the evaluation index system, we should select the factors that affect the eco-geological environment quality the most, carry out the study of the geological environment quality, and finally sum up the 15 evaluation indexes through communication with experts. The principles of index selection are shown in

Table 1. Selection principles of indicators.

Criterion Layer	Indicator Layer	Selection Principle
Geological environment	Slope	The larger the slope, the easier it is for soil erosion to occur, and the greater the loss of machine tillage power [22]. Slope has become an important factor in evaluating landslides and other geological disasters [23].
	Elevation	The area with higher altitude is often the surface watershed. Compared with the adjacent area, the amount of evaporation is large and the soil moisture is low. This also means that the groundwater is very deep, which is not conducive to the growth of vegetation. Low-altitude areas are valley areas with sufficient water resources, which are conducive to vegetation growth [24].
	Soil erosion	This refers to the sensitivity of soil to erosion, which is a relative concept affected by spatial changes, temporal dynamic changes of soil properties, human activities, and other factors. The erosion type in this study area is hydraulic erosion [25].
	Stratigraphy lithology	The lithologic types are complex and varied, and the engineering geological properties are different and play a leading role in the formation, distribution, and activity of geological disasters. Lithology can be divided into four types [26].
	Geological disaster density	Geological disasters are natural disasters mainly caused by geodynamic activities or abnormal changes in the geological environment. The distribution and change law of geological disasters in time and space is not only subject to the natural environment but also related to human activities, and is often the result of the interaction between human beings and nature [27].
	Geomorphic type	According to the morphological characteristics, geomorphic types can be divided into three categories: mountain, hilly, and plain. The main feature of the mountain is undulation; the hill is the transitional type between the mountain and the plain; and the plain refers to terrain with flat ground or slightly undulating ground with a small height difference. As geomorphology is the main basis of human production activities, with the development of production, environmental geomorphology has become an important topic that must be studied in the development of economic production [28].
Ecosystem	Annual rainfall	Under the action of rainfall, rain water can infiltrate into the deep part of a slope along the crack, and the shear strength of the rock and soil decrease after flooding, which leads to the formation of a penetrating slip zone on the contact surface of the soil layer, leading to landslides and other geological disasters [29,30].
	Land use	As all social and economic activities of human beings should be implemented on the land, they should be implemented directly or indirectly through land use. Therefore, the eco-environmental problems caused by human activities are mostly related to land use [31]. Especially in recent years, with increasing human demand, the pressure on land resources has become increasingly prominent, and the eco-environmental problems caused by land use have also become increasingly prominent [32].
	Water distribution	Water conservation is one of the important ecological service functions of terrestrial ecosystems, which includes natural processes such as the atmosphere, moisture, vegetation, and soil, and its changes directly affect regional climate, hydrology, vegetation, and soil. It is an important indicator of regional ecosystem status [33].
	NPP	This is the efficiency of fixing and converting light energy into compounds, which is numerically related to plant growth, development, and reproduction and other life activities. The value of net primary productivity can be used to measure the impact of the regional land use/cover change process on vegetation [34].
	Vegetation cover	The degree of vegetation cover quantifies the density of vegetation and reflects the growth situation of vegetation. It is not only an important parameter to describe the surface vegetation cover but also the basic index to indicate the change in the ecological environment [35]. The rapid development of cities and towns is accompanied by a rapid increase in population and the expansion of construction land, which leads to the loss and destruction of vegetation [36].
Social environment	Road density	The impact of roads on land can be directly reflected in soil; the decline in soil fertility and the loss of soil output function are the most direct manifestations; the impact and destructive effects of roads on hydrological conditions can be directly reflected in the change in surface and underground runoff. When the local subsurface runoff is destroyed, it will lead to a reduction in groundwater stock and vegetation, soil erosion, and agricultural water loss [37]. Therefore, the level of road development directly affects the depth of the ecological effect.
	GDP	The eco-geological environment seriously affects and restricts the normal social and economic development of the study area, the economic level in turn controls the evolution of the regional eco-geological environment, and the areas with high economic levels can better strengthen the restoration and control of geological disasters and reduce disaster risk [38].
	Building distribution	The impact of road construction on the ecological environment is a long-term and changing process, which can have a great impact on the topography, soil structure, ecological environment, and landscape pattern along the line. The degree of building distribution can be used to reflect the interference and destruction of human engineering activities in the regional geological environment [39].
	People density	The greater the population, the greater the demand for materials, that is, the demand for resources will increase, and the amount demanded from the environment will also be greatly increased. If the amount of demand exceeds the amount of environmental renewal, it will lead to ecological destruction; human development and utilization of land and resources has accelerated the degradation of the ecological and geological environment [40].

.

After establishing the index that does not affect the quality of the regional geological environment, the index was classified. The greater the elevation and slope, the steeper the surface, which is more likely to cause geological disasters, which is a negative evaluation index of geological environment quality. The greater the degree of soil erosion, the greater the impact on geological quality, which is also a negative evaluation index. The harder the rock mass, the greater the geological bearing capacity, and it is not easy to cause disasters. Annual rainfall and water distribution have a certain erosion effect on the surface, which is a negative evaluation index. A geological hazard is a negative evaluation index, which has a great impact on the geological environment. The larger the NPP, the better the growth of vegetation; the higher the coverage of vegetation, the better the quality of the ecological environment, both of which are positive indicators. Road density and building density will have an impact on the soil, causing interference and damage to the geological environment to varying degrees, and these two are negative indicators. The higher the GDP, the more the economy can be used to improve the quality of the eco-geological environment, which belongs to a positive index. The higher the population density, the more human production activities take place, which will reduce the quality of the eco-geological environment. The types of geomorphology can be divided into five categories: plain, terrace, hill, small undulating mountain, and mesorelief mountain, which are classified from low to high. Land use types can be divided into five categories: arable land, woodland, grassland, water, and residential land, which are classified from low to high. The indicators are graded quantitatively, with five grades from low to high (

Table 2. Index classification.

Indicators	Hierarchical Assignment
	V1	V2	V3	V4	V5
Slope	<6°	6°–15°	16°–25°	26°–35°	>35°
Elevation (m)	<543	543–681	682–819	820–988	>988
Soil erosion	micro	light	medium	strong	-
Stratigraphy lithology	plain	terrace	hill	small undulating mountain	mesorelief mountain
Geological disaster density	slight	milder	moderate	severe	extremely severe
Geomorphic type	hard rock	soft and hard rock	average hardness	soft rock	-
Annual rainfall (mm)	<956	956–1000	1001–1050	1051–1100	>1100
Land use	arable land	woodland	grassland	water	residential land
Water distribution (km/km2)	<0.01	0.01–0.023	0.024–0.035	0.036–0.05	>0.05
NPP (gC/m2)	<347	348–582	583–754	755–904	>904
Vegetation cover	high coverage	medium coverage	low-to-medium coverage	low coverage	no coverage
Road density (km/km2)	<0.026	0.026–0.068	0.069–0.133	0.134–0.215	>0.215
GDP (million yuan/km2)	>313	205–313	132–204	96–131	<96
Building distribution	none	less	average	many	much
People density (person/km2)	<225	225–275	276–338	339–517	>517

).

2.2.2. Data Sources

The evaluation index of eco-geological environment quality in Hunjiang District was selected from the areas of ecology, geology, and society; it can be divided into a spatial information index and socio-economic index, in which spatial information data are divided into remote sensing image and vector data. Most of these were obtained from the geospatial data cloud sharing platform and the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences. Among them, geological hazard data were obtained from the Jilin Geological Environment Monitoring Station, GDP data were obtained from the Hunjiang Statistical Yearbook, and population data were downloaded from WorldPop. The data sources are shown in

Table 3. Data sources.

Assessment Index	Date Types	Resolution	Data Source
Slope	raster data	30 m	ASTER GDEM
Elevation	raster data	30 m	ASTER GDEM
Soil erosion	raster data	30 m	Resource and Environmental Science and Data Center of the Chinese Academy of Sciences
Stratigraphy lithology	vector data	1:1,000,000	Regional Geological
Geological disaster density	vector data	1:50,000	Jilin Geological Environment Monitoring Station
Geomorphic type	vector data	1:250,000	Resource and Environmental Science and Data Center of the Chinese Academy of Sciences
Annual rainfall	raster data	1 km	1 km monthly precipitation dataset for China
Land use	raster data	30 m	FROM-GLC version 2
Water distribution	vector data	1:250,000	National Geographic Information Resources Catalog Service System
NPP	raster data	30 m	Resource and Environmental Science and Data Center of the Chinese Academy of Sciences
Vegetation cover	raster data	30 m	Landsat 8 OIL
Road density	vector data	1:250,000	National Geographic Information Resources Catalog Service System
GDP	document		Hunjiang Statistical Yearbook
Building distribution	vector data	1:250,000	National Geographic Information Resources Catalog Service System
People density	raster data	1 km	WorldPop

.

On the basis of the original data collection, the topic layer of each evaluation index was established using ArcGIS 10.8 (Esri, Redlands, CA, USA) software, and the coordinate system was transformed and projected uniformly. The coordinate system adopts a unified WGS 1984 UTM 52N area projection coordinate system and a raster data layer with a spatial resolution of 30 m. The processed indicator diagram is shown in Figure 3.

2.3. Determination of Weights

We integrated the indicators in Table 1 into the classification model of eco-geological environmental quality assessment, as shown in Figure 4.

2.3.1. Determining the Subjective Weight of Each Index by FAHP Method

Among all the kinds of analysis and evaluation methods of human subjective cognition, the Analytic Hierarchy Process (AHP) has high superiority and can effectively judge and make decisions on many uncertain qualitative problems which are difficult to analyze quantitatively [41]. However, in the calculation process of the traditional analytic hierarchy process, the construction process of the judgment matrix is too complicated. Thus, it was proposed to introduce a fuzzy consistent matrix to solve people’s subjective fuzzy decision making in the problem, which is called the Fuzzy Analytic Hierarchy Process (FAHP) [42].

(1)

Mathematical theorem of the fuzzy matrix.

$$R={\left(r_{ij}\right)}_{n\times n}=\left[\begin{array}{cccc}{r}_{11}& r_{12}& \cdots & r_{11}\\ r_{11}& r_{11}& \cdots & r_{11}\\ ⋮& ⋮& ⋮& ⋮\\ r_{11}& r_{11}& \cdots & r_{11}\end{array}\right]$$

(1)

A.

If R satisfies $0\le r_{ij}\le 1, \left(i,j=1, 2,\cdots ,n\right)$, then R is a fuzzy matrix.

B.

If the matrix R satisfies the above:

$$r_{ii}=0.5,\left(i=1,2,\cdots ,n\right)$$

(2)

$$r_{ij}=r_{ik}-r_{jk}+0.5, \left(i,j,k=1,2,\cdots ,n\right)$$

(3)

(2)

The basic steps of FAHP are as follows.

A.

Fuzzy Analytic Hierarchy Process score (

Table 4. The factors were compared by the 0.1–0.9 scale method.

Scale	Define	Description
0.5	Just as important	Comparing $a_i$ and $a_j$, they are equally important.
0.6	Slightly more important	Comparing $a_i$ and $a_j$, $a_i$ is slightly more important than $a_j$.
0.7	More important	Comparing $a_i$ and $a_j$, $a_i$ is obviously more important than $a_j$.
0.8	Very important	Comparing $a_i$ and $a_j$, $a_i$ is much more important than $a_j$.
0.9	Absolutely important	Comparing $a_i$ and $a_j$, $a_i$ is absolutely more important than $a_j$.
0.1, 0.2, 0.3, 0.4	Inverse comparison	If the element $a_{i }$ is compared with the element $a_{j }$ to obtain the judgment $r_{ij}$, then the element $a_{j }$ is compared with the element $a_{i }$ to be judged as $r_{ji}=1-r_{ij}$.

).

B.

Construction of the score of the matrix of the Fuzzy Analytic Hierarchy Process.

The Fuzzy Analytic Hierarchy Process scoring matrix A is obtained.

$$A=\left[\begin{array}{cccc}{a}_{11}& a_{12}& \cdots & a_{1n}\\ a_{21}& a_{22}& \cdots & a_{2n}\\ ⋮& ⋮& ⋮& ⋮\\ a_{n1}& a_{n2}& \cdots & a_{nn}\end{array}\right]$$

(4)

• • C.

    Calculate the sum of A.

    $$a_i={{\displaystyle \sum }}_{k=1}^n{a}_{ik},\left(i,k=1,2\cdots n\right)$$

    (5)

    D.

    Find the weight determinant of each factor WI.

    $$w_i=\frac{1}{n}-\frac{1}{2\alpha }+\frac{a_i}{n\alpha }$$

    (6)

    $$WI={\left[w_1{w}_2\cdots w_n\right]}^T$$

    (7)

    E.

    Conformance CI test.

    $$w_{ij}=\alpha \left(w_i-w_j\right)+0.5$$

    (8)

    $$W=\left[\begin{array}{cccc}{w}_{11}& w_{12}& \cdots & w_{1n}\\ w_{21}& w_{22}& \cdots & w_{2n}\\ ⋮& ⋮& ⋮& ⋮\\ w_{n1}& w_{n2}& \cdots & w_{nn}\end{array}\right]$$

    (9)

    $$CI\left(A,W\right)=\frac{{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n\left|w_{ij}-a_{ij}\right|}{n^2}$$

    (10)

It is generally agreed that $CI$ < 0.1 means that the consistency requirements are met.

The weights of the main control factors were calculated according to the relative importance score table of each evaluation index and the FAHP method. The results are shown in

Table 5. Subjective weight.

Target Layer	Criterion Layer	Criterion Layer Weight	Indicator Layer	Indicator Layer Weight	Subjective Weight
Eco-geological environment quality	Geological	0.5333	Slope	0.1244	0.0664
			Elevation	0.1067	0.0569
			Soil erosion	0.1267	0.0676
			Stratigraphy lithology	0.2289	0.1221
			Geological disaster density	0.2489	0.1327
			Geomorphic type	0.1644	0.0877
	Ecosystem	0.3111	Annual rainfall	0.1233	0.0384
			Land use	0.2100	0.0653
			Water distribution	0.1367	0.0425
			NPP	0.2400	0.0747
			Vegetation cover	0.2900	0.0902
	Social	0.1556	Road density	0.2500	0.0389
			GDP	0.1722	0.0268
			Building distribution	0.3111	0.0484
			People density	0.2667	0.0415

.

2.3.2. Determining the Objective Weight of Each Index by CV Method

The coefficient of variation method is a method to calculate the change in degree of each index of the system according to the statistical method, by which, using the information in the index, the weight of the index is calculated objectively [43]. The coefficient of variation method weights each index according to the degree of variation between the current value and the target value of each evaluation index. In the evaluation index system, the greater the difference in the value of the index, that is, the more difficult it is to achieve the index, the more difficult it is for the index to reflect the gap between the evaluated units. The results are shown in

Table 6. Objective weights.

Indicator Layer	Average Value	Standard Deviation	Variable Coefficient	Objective Weight
Slope	3.667	0.816	0.2227	0.0869
Elevation	2.500	0.548	0.2191	0.0855
Soil erosion	2.500	0.548	0.2191	0.0855
Stratigraphy lithology	8.500	0.548	0.0644	0.0251
Geological disaster density	8.667	0.516	0.0596	0.0232
Geomorphic type	5.667	0.816	0.1441	0.0562
Annual rainfall	3.833	0.753	0.1964	0.0766
Land use	6.667	0.816	0.1225	0.0478
Water distribution	4.000	0.894	0.2236	0.0872
NPP	6.667	0.816	0.1225	0.0478
Vegetation cover	8.333	0.816	0.0980	0.0382
Road density	3.167	0.753	0.2377	0.0927
GDP	2.667	0.516	0.1936	0.0755
Building distribution	2.500	0.548	0.2191	0.0855
People density	2.333	0.516	0.2213	0.0863

.

The process of determining objective weights by the coefficient of variation method is as follows:

A.

Collection and arrangement of original value data.

$$X=\left(\begin{array}{cccc}{x}_{11}& x_{12}& \dots & x_{1p}\\ x_{21}& x_{22}& \dots & x_{2p}\\ ⋮& ⋮& ⋮& ⋮\\ x_{n1}& x_{n2}& \dots & x_{np}\end{array}\right)$$

(11)

In the above formula, $X_{ij}$ is the value of the j evaluation parameter of the i evaluation unit.

B.

Mean and standard deviation.

$$\overline{X_j}=\frac{1}{n}{{\displaystyle \sum }}_{i=1}^n{X}_{ij}$$

(12)

$$S_j=\sqrt{\frac{{{\displaystyle \sum }}_{i=1}^n{\left(X_{ij}-\overline{X_{ij}}\right)}^2}{n-1}}$$

(13)

In the above formula, $S_j$ is the standard deviation of the j index; $X_j$ is the average of the j indicator.

C.

Calculate the coefficient of variation of the evaluation index in item j.

$$V_j=\frac{S_j}{\overline{X_j}},j=1,2,\cdots ,p$$

(14)

D.

Coefficient of variation is normalized, and then the weight of each index is obtained.

$$W_j=\frac{V_j}{{{\displaystyle \sum }}_{j=1}^p{V}_j}$$

(15)

In the above formula, $W_j$ is the weight corresponding to the j index.

2.3.3. Improving Game Theory to Determine Comprehensive Weight

The basic idea of the comprehensive weighting method of game theory is to find the linear combination coefficient to minimize the deviation between the comprehensive weight and the weight obtained by different methods. However, the combination coefficient obtained is negative [44]. Therefore, the constraint condition is introduced to improve the comprehensive weighting method of game theory (

Table 7. Combined weights.

Indicator Layer	Subjective Weight	Objective Weight	Combined Weight
Slope	0.0664	0.0869	0.0765
Elevation	0.0569	0.0855	0.0709
Soil erosion	0.0676	0.0855	0.0764
Stratigraphy lithology	0.1221	0.0251	0.0745
Geological disaster density	0.1327	0.0232	0.0789
Geomorphic type	0.0877	0.0562	0.0722
Annual rainfall	0.0384	0.0766	0.0572
Land use	0.0653	0.0478	0.0567
Water distribution	0.0425	0.0872	0.0644
NPP	0.0747	0.0478	0.0615
Vegetation cover	0.0902	0.0382	0.0647
Road density	0.0389	0.0927	0.0653
GDP	0.0268	0.0755	0.0507
Building distribution	0.0484	0.0855	0.0666
People density	0.0415	0.0863	0.0635

). The steps to improve game theory to determine the comprehensive weight are as follows:

A.

Let L methods be used to calculate the weights of the evaluation indexes, and the linear combination formula of comprehensive weight $W_c$ with respect to L weights is established.

$$W_c={{\displaystyle \sum }}_{l=1}^L{\alpha }_l{w}_l^T$$

(16)

In the formula, ${\alpha }_l$ is a linear combination coefficient, and ${\alpha }_l>0$; $w_l^T$ is the transpose of the weight row vector calculated by the first method.

B.

With the goal of minimizing the deviation between the comprehensive vector $W_c$ and all $w_l$, the $W_c$ optimal game model is established.

$$\min \Vert {{\displaystyle \sum }}_{l=1}^L{\alpha }_l{w}_l^T-w_p\Vert ,p=1,2,\cdots ,L$$

(17)

$$\{\begin{array}{l}{\min }_{{\alpha }_1,{\alpha }_2,\cdots ,{\alpha }_L}f={{\displaystyle \sum }}_{p=1}^L\left|\left({{\displaystyle \sum }}_{l=1}^L{\alpha }_l{w}_p{w}_l^T\right)-w_p{w}_p^T\right|\hfill \\ \mathrm{s}.\mathrm{t}. {{\displaystyle \sum }}_{l=1}^L{\alpha }_l^2=1,{\alpha }_l>0\hfill \end{array}$$

(18)

C.

Establishing the Lagrangian function to solve the optimization model.

$$G\left({\alpha }_l,\lambda \right)={{\displaystyle \sum }}_{p=1}^L\left|\left({{\displaystyle \sum }}_{l=1}^L{\alpha }_l{w}_p{w}_p^T\right)\right|+\frac{\lambda }{2}\left({{\displaystyle \sum }}_{l=1}^L{\alpha }_l^2-1\right)$$

(19)

$$\{\begin{array}{l}\frac{\partial G}{\partial {\alpha }_l}={{\displaystyle \sum }}_{p=1}^L\left|w_p{w}_l^T\right|+\lambda {\alpha }_l=0\hfill \\ 2\times \frac{\partial G}{\partial \lambda }={{\displaystyle \sum }}_{l=1}^L{\alpha }_l^2-1=0\hfill \end{array}$$

(20)

D.

The optimal solution of the combined weight coefficient ${\alpha }_l\left({\alpha }_l>0\right)$ weight is obtained.

$${\alpha }_l={{\displaystyle \sum }}_{p=1}^L{w}_p{w}_l^T/\sqrt{{{\displaystyle \sum }}_{l=1}^L{\left({{\displaystyle \sum }}_{p=1}^L{w}_p{w}_l^T\right)}^2}$$

(21)

$$W_{dj}=W_{cj}/{{\displaystyle \sum }}_{j=1}^n{W}_{cj}$$

(22)

2.4. Evaluation Model

On the basis of establishing the classification standard and weight calculation of the index system, the evaluation model of the eco-geological environment quality in Hunjiang District is as follows [45,46]:

$$C_j={{\displaystyle \sum }}_{i=1}^n{w}_i\times e_i$$

(23)

where n is the number of factors involved in the evaluation system; $w_i$ is the weight of evaluation factor i; and $e_i$ represents the standardized value of each index.

3. Results and Discussion

Based on the above evaluation methods and models (Equation (23)), the superposition calculation between the index layers was realized through the grid computing function of ArcGIS, and the assessment score of the eco-geological environment quality in Hunjiang District was obtained. In order to show the evaluation results more intuitively, different thresholds between the grids were used for integration and reclassification in the spatial analysis of ArcGIS, and, combined with the analysis of the change law of the selected indicators, the evaluation score of the eco-geological environment in Hunjiang District can be divided into five grades from high to low: better, good, medium, bad, and worse (

Table 8. Grading standards.

Grade	Worse	Bad	Medium	Good	Better
Value of Grade	<1.79	1.79–2.35	2.36–2.78	2.79–3.12	>3.12

).

From the results of the two weight calculation methods of FAHP and coefficient of variation (Figure 5), it can be seen that the distribution of eco-geological environment quality in the whole region is basically the same (Figure 5), and the overall eco-geological environment quality is good, in which FAHP is subjectively weighted, and the boundary division is more obvious; it can be seen that it is affected by geological hazard density and stratigraphic lithology and other weighting factors, and the weighting of the comparative analysis variation coefficient method is more objective. The graded distribution of eco-geological environmental quality is more uniform and relaxed, and, when combined with the improved game theory, the weights of the two methods are combined to make the results more reliable and authentic (Figure 6).

The results show that the area of better, good, medium, bad, and worse in Hunjiang District is 175.25 km², 389.09 km², 379.32 km², 313.31 km², and 130.62 km² and the area proportion is 12.63%, 28.04%, 27.34%, 22.58%, and 9.41%, respectively (

Table 9. Regional statistics of eco-geological environment quality.

Grade Level	Area (km2)	Area Ratio (%)
Better	175.25	12.63
Good	389.09	28.04
Medium	379.32	27.34
Bad	313.31	22.58
Worse	130.62	9.41

).

According to Figure 6, the areas with good eco-geological environment quality in Hunjiang District are mainly distributed in the northwest, Sandaogou town in the south-central part, Immortal Cave, and Yujiagou in the north-central part, accounting for 40.67% of the study area. This area has high vegetation coverage and relatively flat topography. The land type is mostly cultivated land and woodland, and the stratigraphic lithology is soft rock and relatively soft rock. The population and building density are lower and less affected by internal and external forces, and there are no large-scale geological disasters. The areas of medium quality are mainly distributed in the middle and north, accounting for 27.34% of the total area, which is the transitional area from poor quality to good quality. The area is mainly hilly and with small undulating mountains, with a large population and number of buildings, moderate annual rainfall, average hardness and coverage of lithology, and sporadic distribution of small and medium-sized geological disasters due to regional water flow and human engineering. The poor quality areas are mainly distributed in the vicinity of the Hunjiang River Basin, the southwest boundary, and the northern Hongtuya town, accounting for 31.99% of the study area. The area is densely populated and has many buildings, many river systems, serious soil erosion, and low coverage. Tectonic uplift, river erosion, and unreasonable human engineering excavation lead to frequent geological disasters.

Earnestly protecting the eco-geological environment requires human sustainable development [47]. The Hunjiang River Basin is rich in resources, and various human engineering activities of different degrees of soil erosion are distributed across it. The development of various resources should be strictly controlled, and ecological restoration measures such as afforestation should be combined in dealing with regional environmental problems [48]. Scientific planning is needed for the site selection of various engineering construction projects, so as to achieve the most favorable balance between the engineering construction and eco-geological environment. For the upper and middle quality areas, such as Sandaogou, Yujiagou, and other places, the protection of land, biology, and other resources in the protected areas should be continued, and felling should be prohibited. In addition, it is necessary to strengthen the limited and controllable impact of resource development activities on the regional eco-geological environment, create a good social atmosphere, and promote the smooth progress of various development activities.

4. Conclusions

In the previous evaluation index, the selected evaluation index was too limited. We selected 15 indicators from 3 aspects closely related to eco-geological environment quality to make the evaluation more accurate. In the previous evaluation methods, the calculation of weights is often subjective, which limits the results of the weights. The FAHP method used in this paper can decompose complex problems more clearly and is more suitable for a system with multiple evaluation indicators. In order to overcome the subjectivity of evaluation by FAHP, we combined it with the coefficient of variation method to achieve the internal unity of subjectivity and objectivity, making the evaluation results more scientific and credible. Finally, the grid calculation function of ArcGIS was used to determine the current situation and the spatial pattern of the eco-geological environment quality in the study area, so as to better analyze the study area. The selection of different evaluation indicators may have different influencing factors, resulting in a different eco-geological environmental quality assessment. The method of this study is also applicable to the evaluation of other areas.

To sum up, the upper and middle areas of Hunjiang District account for 68.01% of the total area, and the areas with poor quality account for 31.99% of the total area.

The evaluation of eco-geological environment quality can provide direction for suitable regional ecological protection and construction. On the basis of a comprehensive evaluation, measures such as the rational development and utilization of natural resources such as land and water resources and eco-geological environment protection can be put forward. It is also useful for implementing the development concept of “green development” and “green water and green mountains are Jinshan and Yinshan” and forming a new pattern of resource development with mutual coordination between development and protection as soon as possible.