# Water Resources and Sustainability Assessment Based on Group AHP-PCA Method: A Case Study in the Jinsha River Basin

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Selection of Indicators

#### 2.2. Group Analytic Hierarchy Process (AHP)

#### 2.3. Principal Component Analysis (PCA)

#### 2.4. The Group AHP-PCA

**G**are regarded as the upper triangular elements of the synthesis matrix $L$ in turn. According to the reciprocal properties of the judgment matrix, the lower triangular elements of the synthesis matrix are obtained, shown as:

## 3. Case Study

#### 3.1. Study Area

^{2}, and the length is 2318 km. The basin is located from 90° to 105° E and from 24° to 36° N in the upper reaches of the Yangtze River. In the basin, four well known dams such as Xiangjia Dam, Xiluodu Dam are constructed for flood control, water supply, and hydropower generation. The average rainfall in the study basin is about 960 mm [42]. Due to the increase in population and industrialization, the demands for municipal and industrial water, instream flow, and environmental and recreational water have increased in the basin.

#### 3.2. Data Collection

#### 3.3. Survey

#### 3.4. Application in the Jinsha River Basin Sustainability Assessment

## 4. Dispersion-Based Simulation

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

## Appendix B

Indicators | Data Sources | Website Remarks |
---|---|---|

SWQ | http://www.schj.gov.cn/ | Four provincial environmental protection offices |

http://www.ynepb.gov.cn/ | ||

http://www.qhepb.gov.cn/ | ||

http://www.xzep.gov.cn/ | ||

EC | http://www.stats.gov.cn/ | National Bureau of Statistic of China |

GQ | http://www.schj.gov.cn/ | Four provincial environmental protection offices |

http://www.ynepb.gov.cn/ | ||

http://www.qhepb.gov.cn/ | ||

http://www.xzep.gov.cn/ | ||

EL | http://www.stats.gov.cn/ | National Bureau of Statistic of China |

ICE | ||

IWR | ||

WS | ||

IWU | http://www.mwr.gov.cn/ | Ministry of Water Resources of China |

SC | http://www.stats.gov.cn/ | National Bureau of Statistic of China |

PWSC | ||

IFM | ||

AWU | http://www.mwr.gov.cn/ | Ministry of Water Resources of China |

WQM | http://www.stats.gov.cn/ | National Bureau of Statistic of China |

SWR | http://www.mwr.gov.cn/ | Ministry of Water Resources of China |

WI | http://www.stats.gov.cn/ | National Bureau of Statistic of China |

EB |

Indicators | Unit | Sichuan | Yunnan | Qinghai | Tibet |
---|---|---|---|---|---|

SWQ | Compliance rate | 87.41% | 96.8% | 91% | 100% |

EC | Area classified as the ‘1st’ grade on the eco-nature index/total area | 17.21% | 7.3% | 31.27% | 33.9% |

GQ | Compliance rate | 100% | 100% | 100% | 100% |

EL | Number of persons graduated from high school/population | 0.0060 | 0.0052 | 0.0064 | 0.0060 |

ICE | Investment amount/population (Yuan per person) | 701 | 920 | 1086 | 1192 |

IWR | Investment amount/total area (Yuan/hectare) | 12242 | 9098 | 891 | 484 |

WS | Water saving irrigation area/population (hectare per person) | 3.41 | 3.79 | 3.41 | 7.60 |

IWU | Billion ${\mathrm{m}}^{3}$/total industrial water use × 100 | 68.89 | 26.05 | 3.21 | 1.85 |

SC | Sewage discharge/population number (${\mathrm{m}}^{3}$ per person) | 42.70 | 37.96 | 46.00 | 18.56 |

PWSC | ${\mathrm{m}}^{3}$/per person | 2843.31 | 4391.67 | 10375.95 | 141746.56 |

IFM | Reservoir capacity ${\mathrm{m}}^{3}$/population number (${\mathrm{m}}^{3}$ per person) | 785 | 88 | 34 | 11 |

AWU | Area of irrigated paddy fields/total paddy fields × 100 | 155.90 | 105.20 | 19.90 | 26.90 |

WQM | Achievement rate of objective water quality/annual product per person | 77% | 75% | 74% | 90% |

SWR | Billion ${\mathrm{m}}^{3}$/population number (${\mathrm{m}}^{3}$ per person) | 7.02 | 5.87 | 18.55 | 9.06 |

WI | Amount of annual water intake/annual product per person (${\mathrm{m}}^{3}$/Yuan) | 573 | 673 | 141 | 1335 |

EB | Billions of kilowatt-hours/population number (kilowatt-hours per person) | 3452 | 4775 | 5073 | 1474 |

**Figure A1.**Circle correlation of PCA, where C1 represents environmental conservation, C2 represents maintenance capacity, C3 represents social equity, C4 represents economic efficiency. Data is the synthesis judgments from Table 8, variables are four criteria in this figure.

## Appendix C. Questionnaire Survey

The survey was designed and conducted to determine indicator weights and importance priorities for assessment topics. We are researchers from Huazhong University of Science and Technology, Wuhan, China. We would like to know your attitude towards the elements of water resources and sustainability assessment. Tick in the box after the option you want. |

1. Gender Male Female |

2. What is your name |

3. What is your major occupation? : (a). Manager in environmental conservation; (b). Professor in water resources management; (c). Researcher in social equity; (d). Researcher in water resources development; (e). Professor in economics; (f). Other |

4. For how long have you been in your job?: (a) 1–15 years; (b) 16–30 years; (c) 31 years and above |

5. What is your level of education?: (a). Master; (b) Doctor; (c) Bachelor; (d) Other |

6. You are asked to make comparative judgements on the relative importance of each pair of criteria below. A 9-point scale for measuring the relative importance of each criteria is adopted here for the pairwise comparison. 1 represents equal importance, 3 represents weak importance, 5 represents essential or strong importance, 7 represents demonstrated importance, 9 represents absolute importance.2, 4, 6 and 8 are intermediate values between the two adjacent judgements. Reciprocal values are assigned for each reversed pairwise comparison. |

Environmental conservation and maintenance capacity: (a). 1 2 3 4 5 6 7 8 9 |

Environmental conservation and social equity: (a). 1 2 3 4 5 6 7 8 9 |

Environmental conservation and economic efficiency: (a). 1 2 3 4 5 6 7 8 9 |

Maintenance capacity and social equity: (a). 1 2 3 4 5 6 7 8 9 |

Maintenance capacity and economic efficiency: (a). 1 2 3 4 5 6 7 8 9 |

Economic efficiency: (a). 1 2 3 4 5 6 7 8 9 |

8. You are asked to make comparative judgements on the relative importance of each pair of indicators below by 9-points scale (the indicators part of questionnaire is shown partly as below for word limitation). |

Surface water quality and groundwater quality: (a). 1 2 3 4 5 6 7 8 9 |

Surface water quality and ecosystem conservation: (a). 1 2 3 4 5 6 7 8 9 |

Groundwater quality and ecosystem conservation: (a). 1 2 3 4 5 6 7 8 9 |

Education level and investment in culture and education: (a). 1 2 3 4 5 6 7 8 9 |

Education level and investment in water resources: (a). 1 2 3 4 5 6 7 8 9 |

Education level and water saving: (a). 1 2 3 4 5 6 7 8 9 |

Investment in culture and education and investment in water resources: (a). 1 2 3 4 5 6 7 8 9 |

Investment in culture and education and water saving: (a). 1 2 3 4 5 6 7 8 9 |

Investment in water resources and water saving: (a). 1 2 3 4 5 6 7 8 9 |

Do you have other suggestions about indicators of water resources and sustainability assessment? |

Thank you for your cooperation. |

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**Figure 1.**The calculation process of the group analytic hierarchy process and principal component analysis (group AHP-PCA) method.

**Figure 2.**The hierarchy for water resources and sustainability assessment indicators (adapted from [16]).

**Figure 3.**The broken lines of dispersion with element increase, wherein m represents the number of elements shown in the subfigures (

**a**)–(

**f**).

**Figure 4.**The broken lines of dispersion with dispersion degree increase, wherein m and n vary in the subfigures (

**a**)–(

**f**).

**Table 1.**The indicators of four criteria (adapted from [16]).

Criteria | Indicators | Computational Expressions |
---|---|---|

Environmental conservation | Surface water quality (SWQ) | Integration of items representing surface water quality |

Groundwater quality (GQ) | Integration of items representing groundwater quality | |

Ecosystem conservation (EC) | Integration of items representing groundwater quality | |

Maintenance capacity | Education level (EL) | Number of persons graduated from high school/population |

Investment in culture and education (ICE) | Amount of investment in culture and education/population | |

Investment in water resources (IWR) | Amount of investment in water resources/total area | |

Water saving (WS) | Amount of water saved/population | |

Social equity | Industrial water use (IWU) | Industrial water use in a watershed/total industrial water use × 100 |

Sewage coverage (SC) | Number of persons using sewage/population × 100 | |

Potable water system coverage (PWSC) | Number of persons using potable water system/population × 100 | |

Instream flow management (IFM) | Reservoir capacity/population | |

Agricultural water use (AWU) | Area of irrigated paddy fields/total paddy fields × 100 | |

Economic efficiency | Water-quality management (WQM) | Achievement rate of objective water quality/annual product per person |

Supplementary water resources (SWR) | Amount of supplementary water resources/population | |

Water intake (WI) | Amount of annual water intake/annual product per person | |

Economic benefits (EB) | Annual economic benefits from water resources/population |

**Table 2.**Scale measurement for AHP (adapted from [13]).

Numerical Values | Definition |
---|---|

1 | Index ${x}_{i}$ is as important as index ${x}_{j}$ |

3 | Index ${x}_{i}$ is slightly more important than index ${x}_{j}$ |

5 | Index ${x}_{i}$ is obviously more important than index ${x}_{j}$ |

7 | Index ${x}_{i}$ is strongly more important than index ${x}_{j}$ |

9 | Index ${x}_{i}$ is extremely more important than index ${x}_{j}$ |

2, 4, 6, 8 | Middle value of the above adjacent judgments |

**Table 3.**The group judgment matrix $A$. ${a}_{ij}{}^{k}$ is the k-th expert judgment between i and j. n is element number and m is expert number.

A | x_{1} | x_{2} | … | x_{n} |

x_{1} | 1 | $({a}_{12}^{1}\text{}\dots \text{}{a}_{12}^{k}\text{}{a}_{12}^{m}\text{})$ | … | $({a}_{1n}^{1}\text{}\dots \text{}{a}_{1n}^{k}\text{}{a}_{1n}^{m})$ |

x_{2} | $({a}_{21}^{1}\text{}\dots \text{}{a}_{21}^{k}\text{}{a}_{21}^{m}\text{})$ | 1 | … | $({a}_{2n}^{1}\text{}\dots \text{}{a}_{2n}^{k}\text{}{a}_{2n}^{m}\text{})$ |

… | … | … | … | … |

x_{n} | $({a}_{n1}^{1}\text{}\dots \text{}{a}_{n1}^{k}\text{}{a}_{n1}^{m}\text{})$ | $({a}_{n2}^{1}\text{}\dots \text{}{a}_{n2}^{k}\text{}{a}_{n2}^{m}\text{})$ | … | 1 |

**Table 4.**Profiles of 5 experts. Sources are derived from questionnaires in Appendix C.

ID | Gender | Working Seniority | Profession | Education |
---|---|---|---|---|

1 | Male | 15 | Manager in environmental conservation | Master |

2 | Female | 10 | Professor in water resources management | Doctor |

3 | Male | 17 | Researcher in social equity | Bachelor |

4 | Female | 15 | Researcher in water resources development | Master |

5 | Male | 20 | Professor in economics | Doctor |

**Table 5.**Group judgment matrix between criteria with respect to the goal. These data are judgments on 4 criteria provided by the five aforementioned experts.

C_{1} | C_{2} | C_{3} | C_{4} | |

C_{1} | 1 | $(7,\text{}3,\text{}1,\text{}1,\text{}3)$ | $(1,\text{}3,\text{}3\text{},\text{}1,\text{}5)$ | $(7,\text{}1,\text{}3,\text{}5,\text{}1)$ |

C_{2} | 1 | $(\frac{1}{3},\text{}\frac{1}{5},\text{}\frac{1}{7},\text{}1,\text{}\frac{1}{3})$ | $(1,\text{}\frac{1}{3},\text{}\frac{1}{5},\text{}1,\text{}\frac{1}{7})$ | |

C_{3} | 1 | $(7,\text{}1,\text{}1,\text{}3,\text{}5)$ | ||

C_{4} | 1 |

C_{1} | C_{2} | C_{3} | C_{4} | Weight | |

C_{1} | $1$ | $3.2359$ | $3.2389$ | $2.7856$ | $0.4449$ |

C_{2} | $\frac{1}{3.2359}$ | $1$ | $0.2718$ | $0.3759$ | $0.0883$ |

C_{3} | $\frac{1}{3.2389}$ | $\frac{1}{0.2718}$ | $1$ | $3.5722$ | $0.3155$ |

C_{4} | $\frac{1}{2.7856}$ | $\frac{1}{0.3759}$ | $\frac{1}{3.5722}$ | $1$ | $0.1512$ |

**Table 7.**The synthesis results of the assessment. These data are the results of expert judgment calculated by the proposed method or the traditional group AHP method.

Criteria | Weights (Group AHP-PCA) | Weights (Group AHP) | Indicators | Global Weights (Group AHP-PCA) | Rank (Group AHP-PCA) | Global Weights (Group AHP) | Rank (Group AHP) |
---|---|---|---|---|---|---|---|

Environmental conservation | 0.4449 | 0.4026 | SWQ | 0.2167 | 1 | 0.2537 | 1 |

EC | 0.0387 | 8 | 0.1167 | 3 | |||

GQ | 0.1121 | 3 | 0.0539 | 7 | |||

Maintenance capacity | 0.0883 | 0.1027 | EL | 0.0083 | 16 | 0.0089 | 16 |

ICE | 0.0230 | 12 | 0.0275 | 12 | |||

IWR | 0.0322 | 9 | 0.0327 | 9 | |||

WS | 0.0249 | 11 | 0.0303 | 10 | |||

Social equity | 0.3155 | 0.2967 | IWU | 0.1215 | 2 | 0.1215 | 2 |

SC | 0.0603 | 6 | 0.0603 | 6 | |||

PWSC | 0.0288 | 10 | 0.0288 | 11 | |||

IFM | 0.0138 | 14 | 0.0138 | 14 | |||

AWU | 0.0802 | 4 | 0.0802 | 4 | |||

Economic efficiency | 0.1513 | 0.1608 | WQM | 0.0629 | 5 | 0.0629 | 5 |

SWR | 0.0439 | 7 | 0.0439 | 8 | |||

WI | 0.0271 | 13 | 0.0271 | 13 | |||

EB | 0.0129 | 15 | 0.0129 | 15 |

Indicators | Global Weights | Sichuan | Yunnan | Qinghai | Tibet |
---|---|---|---|---|---|

SWQ | 0.2167 | 0.2330 | 0.2580 | 0.2425 | 0.2665 |

EC | 0.0387 | 0.1919 | 0.0814 | 0.3487 | 0.3780 |

GQ | 0.1121 | 0.2500 | 0.2500 | 0.2500 | 0.2500 |

EL | 0.0083 | 0.2542 | 0.2203 | 0.2712 | 0.2542 |

ICE | 0.0230 | 0.1798 | 0.2360 | 0.2785 | 0.3057 |

IWR | 0.0322 | 0.5390 | 0.4005 | 0.0392 | 0.0213 |

WS | 0.0249 | 0.1873 | 0.2081 | 0.1872 | 0.4174 |

IWU | 0.1215 | 0.6889 | 0.2605 | 0.0321 | 0.0185 |

SC | 0.0603 | 0.2940 | 0.2614 | 0.3168 | 0.1278 |

PWSC | 0.0288 | 0.0178 | 0.0276 | 0.0651 | 0.8895 |

IFM | 0.0138 | 0.8551 | 0.0959 | 0.0370 | 0.0120 |

AWU | 0.0802 | 0.5063 | 0.3417 | 0.0646 | 0.0874 |

WQM | 0.0629 | 0.2437 | 0.2373 | 0.2342 | 0.2848 |

SWR | 0.0439 | 0.1733 | 0.1449 | 0.4580 | 0.2237 |

WI | 0.0271 | 0.2105 | 0.2472 | 0.0518 | 0.4904 |

EB | 0.0129 | 0.2336 | 0.3232 | 0.3434 | 0.0998 |

Final Scores | 0.3002 | 0.2215 | 0.1799 | 0.2057 |

**Table 9.**Initial matrix. These data in this matrix are simulation data which represent 3 experts’ initial judgment on 4 pairwise compared elements.

1 | (3, 3, 3) | (5, 5, 5) | (5, 5, 5) |

1 | $(\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3})$ | $(\frac{1}{5}\text{},\text{}\frac{1}{5},\text{}\frac{1}{5})$ | |

1 | $(\frac{1}{7}\text{},\text{}\frac{1}{7},\text{}\frac{1}{7})$ | ||

1 |

**Table 10.**Initial matrix. These data in this matrix are simulation data which represent 9 experts’ initial judgments on 7 pairwise compared elements.

1 | (3, 3, 3, 3, 3, 3, 3, 3, 3) | (5, 5, 5, 5, 5, 5, 5, 5, 5) | (7, 7, 7, 7, 7, 7, 7, 7, 7) | $(\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3})$ | $(\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5})$ | $(\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7})$ |

1 | $(5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5)$ | $(7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7)$ | $(3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3)$ | $(7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7)$ | $(\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7},\text{}\frac{1}{7})$ | |

1 | $(\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3})$ | $(3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3)$ | $(5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5)$ | $(\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5})$ | ||

1 | $(7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7)$ | $(\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3},\text{}\frac{1}{3})$ | $(5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5,\text{}5)$ | |||

1 | $(\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5},\text{}\frac{1}{5})$ | $(3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3,\text{}3)$ | ||||

1 | $(7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7,\text{}7)$ | |||||

1 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhou, J.-L.; Xu, Q.-Q.; Zhang, X.-Y.
Water Resources and Sustainability Assessment Based on Group AHP-PCA Method: A Case Study in the Jinsha River Basin. *Water* **2018**, *10*, 1880.
https://doi.org/10.3390/w10121880

**AMA Style**

Zhou J-L, Xu Q-Q, Zhang X-Y.
Water Resources and Sustainability Assessment Based on Group AHP-PCA Method: A Case Study in the Jinsha River Basin. *Water*. 2018; 10(12):1880.
https://doi.org/10.3390/w10121880

**Chicago/Turabian Style**

Zhou, Jian-Lan, Qin-Qin Xu, and Xian-Yong Zhang.
2018. "Water Resources and Sustainability Assessment Based on Group AHP-PCA Method: A Case Study in the Jinsha River Basin" *Water* 10, no. 12: 1880.
https://doi.org/10.3390/w10121880