JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (7): 67-79.doi: 10.6040/j.issn.1671-9352.4.2022.9768

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Extension sequential three-way decision model and its application

Junyu WANG1,2,3(),Yafeng YANG1,2,3,Jingxuan XUE1,Lihong LI1,2,3,*()   

  1. 1. College of Science, North China University of Science and Technology, Tangshan 063210, Hebei, China
    2. Hebei Province Key Laboratory of Data Science and Application, Tangshan 063210, Hebei, China
    3. Tangshan Key Laboratory of Engineering Computing, Tangshan 063210, Hebei, China
  • Received:2022-08-09 Online:2023-07-20 Published:2023-07-05
  • Contact: Lihong LI E-mail:1784557362@qq.com;22687426@qq.com

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Abstract:

The basic method of extension evaluation and the "rule by three divisions" decision-making idea are integrated, and the sequential idea is introduced to construct an extension sequential three-way decision model and realize the purpose of dynamic decision-making and mining optimization indicators. Firstly, the data is standardized and the weight of attributes is calculated. The extension evaluation method is used as the evaluation criterion of the three-way decisions, new decision rules are defined, and the rationality of dividing the three domains is explained. Then, according to the attribute weight, the sequential evaluation attributes of multiple granularity are obtained, the multi-stage three-way decision is made, and the decision results are given. Finally, according to the decision-making results, the indicators that cause changes in sample division are analyzed, and optimization suggestions are put forward. The model was applied to the analysis of water resources carrying capacity, and compared with the entropy weight matter-element extension decision-making model. The results show that the evaluation results obtained by using the extension sequential three-way decision model and the entropy weight matter-element extension decision-making model are basically consistent, the accuracy rate reaches 84.55%, which verifies the validity and practicability of the model.

Key words: sequential three-way decision, extension evaluation, granulation, carrying capacity of water resource

CLC Number: 

  • TP181

Fig.1

Framework of three-way decision"

Table 1

Evaluation grades and standards of water resources carrying capacity in the Yangtze River Economic Belt"

评价指标 性质 评价等级及标准值
Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ
人均水资源量/m3 正 80~500 500~1 000 1 000~2 000 2 000~3 000 3 000~5 500
农业用水占比/% 负 60~100 55~60 50~55 30~50 0~30
生活用水占比/% 正 0~4 4~5 5~7 7~10 10~100
人均库容量/m3 正 0~200 200~400 400~600 600~800 800~2 200
生态环境用水占比/% 正 0~1 1~2 2~3 3~5 5~10
污水处理率/% 正 0~40 40~50 50~80 80~95 95~100
万元GDP化学需氧量排放量/kg 负 3~7 2~3 1~2 0.5~1 0~0.5
建成区绿化覆盖率/% 正 0~20 20~30 30~35 35~40 40~50
森林覆盖率/% 正 0~10 10~20 20~40 40~50 50~100
湿地总面积占国土面积比重/% 正 0~2 2~4 4~6 6~8 8~74
造林面积/hm2 正 0~60 000 60 000~170 000 170 000~280 000 280 000~480 000 480 000~720 000
第一产业占GDP比值/% 负 20~15 13~15 10~13 5~10 0~5
第二产业占GDP比值/% 负 52~56 48~52 44~48 40~44 0~40
农业灌溉亩均用水量/m3 负 800~1 000 600~800 400~600 200~400 0~200

Table 2

Weight of each indicator"

符号指标权重
c1 湿地总面积占国土面积比重/% 0.099 0
c2 森林覆盖率/% 0.093 7
c3 造林面积/hm2 0.092 4
c4 生活用水占比/% 0.087 6
c5 农业用水占比/% 0.087 1
c6 人均库容量/m3 0.083 7
c7 第一产业占GDP比值/% 0.082 2
c8 万元GDP化学需氧量排放量/kg 0.075 5
c9 人均水资源量/m3 0.072 9
c10 第二产业占GDP比值/% 0.064 8
c11 生态环境用水占比/% 0.057 5
c12 农业灌溉亩均用水量/m3 0.052 9
c13 污水处理率/% 0.026 4
c14 建成区绿化覆盖率/% 0.024 3

Table 3

Correlation between water resources carrying capacity indicators and grades in the Yangtze River Economic Belt in 2018"

省级行政区 评价指标 关联度
K1(x0) K2(x0) K3(x0) K4(x0) K5(x0)
上海 c1 -0.989 9 -0.989 6 -0.989 3 -0.988 9 0.011 1
c2 -0.223 5 0.404 0 -0.298 0 -0.649 0 -0.719 2
江苏 c1 -0.481 1 -0.460 8 -0.438 8 -0.414 9 0.295 6
c2 -0.254 9 0.480 0 -0.240 0 -0.620 0 -0.696 0
浙江 c1 -0.449 5 -0.387 8 -0.310 4 -0.210 6 0.044 1
c2 -0.549 2 -0.492 9 -0.323 8 -0.188 6 0.188 6
安徽 c1 -0.422 6 -0.316 8 -0.163 7 0.270 0 -0.067 5
c2 -0.394 3 -0.231 9 0.432 5 -0.283 8 -0.427 0
江西 c1 -0.387 6 -0.210 1 0.275 0 -0.091 7 -0.318 8
c2 -0.568 4 -0.514 5 -0.352 7 -0.223 2 0.223 2
湖北 c1 -0.426 1 -0.326 7 -0.185 5 0.115 0 -0.028 8
c2 -0.427 8 -0.331 1 0.019 5 -0.009 8 -0.207 8
湖南 c1 -0.368 8 -0.144 1 0.405 0 -0.198 3 -0.398 8
c2 -0.444 1 -0.374 0 -0.163 2 0.031 0 -0.006 2
重庆 c1 -0.168 9 0.255 0 -0.372 5 -0.581 7 -0.686 3
c2 -0.434 4 -0.349 0 -0.067 3 0.311 0 -0.137 8
四川 c1 -0.308 4 0.195 0 -0.097 5 -0.398 3 -0.548 8
c2 -0.424 3 -0.321 6 0.098 5 -0.049 3 -0.239 4
贵州 c1 0.405 0 -0.405 0 -0.702 5 -0.801 7 -0.851 3
c2 -0.435 5 -0.351 9 -0.079 3 0.377 0 -0.124 6
云南 c1 0.285 0 -0.285 0 -0.642 5 -0.761 7 -0.821 3
c2 -0.500 4 -0.438 0 -0.250 7 -0.100 8 0.100 8

Table 4

Correlation between water resources carrying capacity indicators and grades in Shanghai from 2009 to 2018"

年份 评价指标 关联度
K1(x0) K2(x0) K3(x0) K4(x0) K5(x0)
2009 c1 -0.717 8 -0.709 7 -0.701 2 -0.692 1 0.307 9
c2 0.059 0 -0.059 0 -0.529 5 -0.764 8 -0.811 8
2010 c1 -0.717 8 -0.709 7 -0.701 2 -0.692 1 0.307 9
c2 0.059 0 -0.059 0 -0.529 5 -0.764 8 -0.811 8
2011 c1 -0.717 8 -0.709 7 -0.701 2 -0.692 1 0.307 9
c2 0.059 0 -0.059 0 -0.529 5 -0.764 8 -0.811 8
2012 c1 -0.717 8 -0.709 7 -0.701 2 -0.692 1 0.307 9
c2 0.059 0 -0.059 0 -0.529 5 -0.764 8 -0.811 8
2013 c1 -0.989 9 -0.989 6 -0.989 3 -0.988 9 0.011 1
c2 -0.064 5 0.074 0 -0.463 0 -0.731 5 -0.785 2
2014 c1 -0.989 9 -0.989 6 -0.989 3 -0.988 9 0.011 1
c2 -0.064 5 0.074 0 -0.463 0 -0.731 5 -0.785 2
2015 c1 -0.989 9 -0.989 6 -0.989 3 -0.988 9 0.011 1
c2 -0.064 5 0.074 0 -0.463 0 -0.731 5 -0.785 2
2016 c1 -0.989 9 -0.989 6 -0.989 3 -0.988 9 0.011 1
c2 -0.064 5 0.074 0 -0.463 0 -0.731 5 -0.785 2
2017 c1 -0.989 9 -0.989 6 -0.989 3 -0.988 9 0.011 1
c2 -0.064 5 0.074 0 -0.463 0 -0.731 5 -0.785 2
2018 c1 -0.989 9 -0.989 6 -0.989 3 -0.988 9 0.011 1
c2 -0.223 5 0.404 0 -0.298 0 -0.649 0 -0.719 2

Table 5

Evaluation values of water resources carrying capacity in the Yangtze River Economic Belt in 2018"

省级行政区 综合关联度 等级
K1(xi) K2(xi) K3(xi) K4(xi) K5(xi)
上海 -0.617 2 -0.312 0 -0.653 2 -0.823 7 -0.344 0 Ⅱ
江苏 -0.371 1 -0.003 4 -0.342 1 -0.514 6 -0.186 5 Ⅱ
浙江 -0.498 0 -0.438 9 -0.316 9 -0.199 9 0.114 4 Ⅴ
安徽 -0.408 8 -0.275 5 0.126 2 0.000 8 -0.242 3 Ⅲ
江西 -0.475 6 -0.358 1 -0.030 2 -0.155 6 -0.055 2 Ⅲ
湖北 -0.426 9 -0.328 9 -0.085 8 0.054 3 -0.115 8 Ⅳ
湖南 -0.405 4 -0.255 9 0.128 7 -0.086 8 -0.207 9 Ⅲ
重庆 -0.298 0 -0.038 7 -0.224 1 -0.147 6 -0.419 6 Ⅱ
四川 -0.364 8 -0.056 2 -0.002 2 -0.228 6 -0.398 3 Ⅲ
贵州 -0.003 7 -0.379 2 -0.399 5 -0.228 6 -0.497 9 Ⅰ
云南 -0.096 9 -0.359 4 -0.452 0 -0.440 3 -0.372 9 Ⅰ

Table 6

Evaluation values of water resources carrying capacity in Shanghai from 2009 to 2018"

年份 综合关联度 等级
K1(xi) K2(xi) K3(xi) K4(xi) K5(xi)
2009 -0.340 1 -0.393 3 -0.617 7 -0.727 4 -0.236 5 Ⅴ
2010 -0.340 1 -0.393 3 -0.617 7 -0.727 4 -0.236 5 Ⅴ
2011 -0.340 1 -0.393 3 -0.617 7 -0.727 4 -0.236 5 Ⅴ
2012 -0.340 1 -0.393 3 -0.617 7 -0.727 4 -0.236 5 Ⅴ
2013 -0.539 9 -0.472 4 -0.733 4 -0.863 8 -0.376 1 Ⅴ
2014 -0.539 9 -0.472 4 -0.733 4 -0.863 8 -0.376 1 Ⅴ
2015 -0.539 9 -0.472 4 -0.733 4 -0.863 8 -0.376 1 Ⅴ
2016 -0.539 9 -0.472 4 -0.733 4 -0.863 8 -0.376 1 Ⅴ
2017 -0.539 9 -0.472 4 -0.733 4 -0.863 8 -0.376 1 Ⅴ
2018 -0.617 2 -0.312 0 -0.653 2 -0.823 7 -0.344 0 Ⅱ

Fig.2

Results of the first decision on water resources carrying capacity"

Fig.3

Results of the second decision on water resources carrying capacity"

Fig.4

Results of the third decision on water resources carrying capacity"

Fig.5

Results of the fourth decision on water resources carrying capacity"

Fig.6

Results of the fifth decision on water resources carrying capacity"

Fig.7

Results of the sixth decision on water resources carrying capacity"

Fig.8

Results of the seventh decision on water resources carrying capacity"

Fig.9

Comparison of decision-making results"

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