World Applied Programming, Vol (3), Issue (10), October 2013. 522-528 ISSN: 2222-2510 ©2013 WAP journal. www.tijournals.com
Formulating Forest Management Strategies Using ELECTRE Method (Case Study: District 2 Nav, Asalem, Guilan, Iran) Tooba Abedi
Mohammad Ghamgosar
Institute of Environmental Research, Academic Center for Education, Culture and Research (ACECR), Rasht, Iran.
[email protected]
Institute of Environmental Research, Academic Center for Education, Culture and Research (ACECR), Rasht, Iran.
[email protected]
Abstract: In this paper, we determine effective management strategy for Hyrcanian forest in the Asalem of Guilan Province by Multi-criteria decision making method. ELECTRE method is used in this study that programming it in mat lab. Easiness to use and understand the method, and interpretability of the results are important qualities of the method applied. Three strategies were formulated for the study area including Commercial strategy, Forest recreation and Conservation strategy with 3 criteria as business, forest recreation values and nature conservation values and 14 criterion variables containing area of commercial forests, revenues of products, volume of cutting from commercial forests, stand stock, area of productive forests, costs of cutting, area of recreational forests, area of commercial forests with recreational values, area of closed forest, area of conservation forest, area of commercial forests with conservation values, dead wood volume, natural reserves and area of old forests. Ranking the strategies based on ELECTRE method show Conservation strategy is prefer as the best management strategy for the study area. Commercial strategy is in second rank and Recreational strategy the last one is. Keywords: Forest management strategy, Multi-Criteria Decision Making, Management planning, ELECTRE I.
INTRODUCTION
Forest resource planning is a very complex problem mainly due to the multiplicity of wide-ranging criteria involved in the underlying decision-making process (Balteiro & Romero, 2008). The aim in forest planning is to provide support for forestry decision making so that an efficient strategy best fulfilling the objectives set for the management of the forest area under planning can be found (Kangas et.al, 2001b). Decision-makers have not only economic objectives but also those of amenity and non-market values of recreation and nature conservation, for instance. Research on natural resources planning has answered these challenges by applying and developing decision support methods and techniques for multiple criteria and participatory planning (Kangas et.al, 2001a). Multi-objectivity is typical for current forestry: forests should produce reasonable incomes while at the same time promoting conservation and recreational considerations. Criteria other than those related to wood production have been given more and more weight in the choice of management alternatives (Kangas et.al, 2001b). The forest planning is just base on commercial value and cutting in northern forest of Iran (Hyrcanian forest), then we seek to found a multi objective method to formulated forest strategies to contain all values of the forest. Outranking methods serve as one alternative for approaching complex choice problems with multiple criteria and multiple participants. Outranking indicates the degree of dominance of one alternative over another (Kangas et.al, 2001a).
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Tooba Abedi and Mohammad Ghamgosar. World Applied Programming, Vol (3), No (10), October 2013.
The ELECTRE method that is utilized in this study can be considered as a non-compensatory model. It means that a really bad score of any alternative with respect to any one criterion cannot necessarily be compensated for by good scores in other criteria. The ELECTRE methods have originally been developed by Bernard Roy (1968). Several versions of the ELECTRE method have been presented for different situations: ELECTRE I and IS are designed for selection problems, ELECTRE TRI for sorting problems and ELECTRE II, III and IV for ranking problems. ELECTRE II is an older version, where an abrupt change from indifference to strict preference is assumed instead of pseudo-criteria (Al-Shemmeri et al., 1997). We use ELECTRE I in this study. Bertier and Montgolfier (1974) applied the ELECTRE method to the choice of a suburban highway design inside a forest area. Salminen et al. (1998) compared the performance of the PROMETHEE, SMART, and ELECTRE III methods specifically because of their suitability in the context of environmental decision-making. The authors found little difference in performance between SMART and PROMETHEE, but felt that ELECTRE III had some extra functionality. Leyva-López and Fernández-González (2003) conducted a comparative study of PROMETHEE II for group decision with an extension of the ELECTRE III multi-criteria outranking methodology. Gilliams et al. (2005) compared AHP with other discrete multi-criteria methods (i.e., ELECTRE, and three types of the PROMETHEE approach) in a case where the purpose of the research was to choose the best afforestation alternative in Belgium. These alternatives were different afforestation practices, locations and the length of the afforestation period. The authors concluded that, for some issues, PROMETHEE worked slightly better than the other two methods. Pauwels et al. (2007) resorted to ELECTRE for comparing several silvicultural alternatives of Larix stands in Belgium taking into account criteria related to biodiversity and stability when the stands faced potential windstorm damage. Hayashida et al. (2010) examined effective policies for financing and activities to select activity of the forest on Mount Ryuoh in the city of Higashi-Hiroshima by multi attribute utility analysis. They deal with decision making problems with multiple attributes and select the most effective solution among several alternatives by deriving preference of the decision maker. They used several alternatives of social systems for financing for preservation of the forest in which all the people receiving the benefit from the forest and proposed preservation the forest. II.
MATERIAL AND METHOD
Study area The study area is district 2 of Nav includes 3527 ha of public forest in the Hyrcanian forest belt with an altitudinal range from 280 to 2120 a.s.l and geographic coordination is 37⁰ 37΄ 23˝ to 37⁰ 42́ 31˝ north and 48⁰ 44΄ 36˝ to 48⁰ 49΄ 58˝ . It is located in west of Guilan province in Asalem. The forest type is Fagetum-orientalis with glorious landscape of cloudy in high altitude. Method Fagetum community is an important communities of hardwood deciduous forest of north of Iran. Therefore, their biodiversity and wood production is very important for implementing of forest planning based on close to nature forestry. To determine a suitable management strategy to obtain the success for the forest planning is considerable. Then, in this study, initially, three strategies following different scenarios were formulated in the planning project of Nav forest of Guilan. The impacts of the strategies were measured by numeric criterion variables and they were estimated through planning calculations. The criteria were selected by group decision making experts meeting and their values were extracted from Forest Management Plan of District 2 of Nav that accessible from Administration of Natural Resources and Watershed Management of Guilan. The ‘Commercial strategy’ (A1) emphasized economical aspects, the ‘Forest recreation’ (A2) and ‘Conservation strategy’ (A3) emphasized the related goals, respectively, Including 3 criteria and 14 criterion variables (table 1).
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Tooba Abedi and Mohammad Ghamgosar. World Applied Programming, Vol (3), No (10), October 2013.
TABLE I. The strategies, criteria and their variables applied in Forest of Nav Strategy Commercial
Criterion Business
Criterion variable Area of commercial forests Revenues of products Volume of cutting from commercial forests Stand stock Area of productive forests Costs of cutting
Recreational
Forest recreation values
Area of recreational forests Area of commercial forests with recreational values
Conservation
Area of closed forest Area of conservation forest Area of commercial forests with conservation values Dead wood volume Area of old forests Natural reserves
Nature conservation values
The values of criterion variables – measured in their own units (m3, monetary units and hectares etc.) – which were scaled on a fixed interval between 1 and 10, ascending. Benefits related to business revenues were measured by using the area of commercial forests, revenues of products, volume of cutting from commercial forests, stand stock, area of productive forests and costs of cutting. Forest recreation values were measured by the area of recreational forests, area of commercial forests with recreational values and area of closed forest. The third main criterion was the nature conservation value, measured by the area of conservation forest, area of commercial forests with conservation values, dead wood volume, area of old forests and natural reserves. ELECTRE I should be applied only when all the criteria have been coded in numerical scales with identical ranges. In such a situation we can assert that an action “a outranks b” (that is, “a is at least as good as b”). The ranking relationship between the two alternatives Ak and A1 are denoted as Ak ® Al if alternative-k no-one dominates the alternative to the quantitative, thus better decision makers to take risks Ak than Al. Pairwise comparison of each alternative in each criteria is expressed by values (Xij). This value must be normalized to a scale comparable to (rij) (Ermatita et. al, 2011). This value is calculated with the formula as below (Asgharpour, 2011; Ermatita et. al, 2011): rij
X ij m
i 1,2, , m, j 1, 2, , n
(1)
X ij2
i 1
V matrix is normalized weighting decision matrix that calculated based on the equation: Vij w j X ij
(2)
Concordance index (Ckl) that shows the sum of weights of criteria, according to the formula;
C k l j v lj v lj
j 1,2, , n
(3)
Calculating the value set for the matrix discordance is the following:
D k l j v lj v lj
j 1,2, , n
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(4)
Tooba Abedi and Mohammad Ghamgosar. World Applied Programming, Vol (3), No (10), October 2013.
Concordance index is relative importance of the different criteria. C k l Concordance matrix elements calculated using the formula: Ck l
w
(5)
j
jC kl
d kl Discordance matrix elements calculated using the formula: dkl
max v kj v ij
jD
max v kj v ij
kl
(6)
jJ
The discordance indices of different criteria are not aggregated using the weights, since one discordant criterion is sufficient to discard outranking. The dominance concordance matrix is calculated by threshold for concordance indices. The threshold is shown by c , k is dominant to l when C kl c . The threshold value can be average of concordance indices. Dominance concordance matrix calculated by the following formula: m
m
c c
k,l
k 1 l1
mm 1
(7)
We use c 0.5 in this study. The dominance discordance matrix is calculated by threshold for discordance indices. The threshold is shown by d calculated using the formula: m
m
d d
k,l
k 1 l 1
mm 1
(8)
We use it as d 1 in this study. Concordance matrix elements calculated based on the dominant: 1, fkl 0,
c kl c c kl c
(9)
Discordance matrix elements calculated based on the dominant: 1, gkl 0,
d kl d d kl d
(10)
Dominant aggregate matrix is calculated by intersection of F and G: h k l f kl .g kl
(11)
Finally it should be eliminated the low utility alternatives. Matrix H is general matrix of the preference in this study. From the results of calculations the above formula are programmed in math lab.
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Tooba Abedi and Mohammad Ghamgosar. World Applied Programming, Vol (3), No (10), October 2013.
III.
RESULT
The multi-criteria decision making (MCDM) results were calculated with the outranking method ELECTRE. By using the weights of criteria that calculated as V matrix (formula 2), the global weights (priorities) were calculated for the other decision elements in respect to the upper level elements related to them (table 2). The best alternative in the analysis proved to be the conservation strategy. TABLE II. The criterion variables and their priorities Criterion variable Area of commercial forests Revenues of products Volume of cutting from commercial forests Stand stock Area of productiveilized forests Costs of cutting Area of recreational forests Area of commercial forests with recreational values Area of closed forest Area of conservation forest Area of commercial forests with conservation values Dead wood volume Area of old forests Natural reserves
Priority 0.0797 0.0806 0.0806 0.0806 0.0774 0.0806 0.0790 0.0790 0.0780 0.0760 0.0768 0.0170 0.0750 0.0399
The global weights of the criteria were: business (0.4795), forest recreation values (0. 236) and conservation values (0.2847). The best alternative in the analysis proved to be the conservation strategy. TABLE III. Concordance and discordance sets obtained by formula (3) and (4) Concordance set ( C kl )
C12
1
2
3
4
5
11
C13
1
2
3
4
5
8
C21
6
7
8
9
10
11
C23
1
2
3
7
8
9
C31
6
7
9
10
11
C32
1
2
3
4
5
Discordance set ( D kl )
13
12
14
12
13
14
10
11
12
13
14
D12
7
8
9
10
12
14
D13
6
7
8
9
10
11
D 21
1
2
3
4
5
13
D 23
4
5
6
10
11
12
D 31
1
2
3
4
5
D32
6
7
8
9
Concordance matrix is calculated using formula (7): 0.5507 0.4068 C 0.4552 0.4085 0.5223 0.6836
Discordance matrix is calculated using formula (8): 1.0 1.0 D 1.0 0.97 1.0 1.1
F matrix is calculated using formula (9):
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12
13
13
14
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Tooba Abedi and Mohammad Ghamgosar. World Applied Programming, Vol (3), No (10), October 2013.
0 1 0 F 0 0 0 1 1 0
Elements of the matrix F is determined as the dominant discordance. G matrix is calculated using formula (10): 0 1 1 G 1 0 1 1 1 0
Elements with value 1 in matrix of G are show dominance relationship between alternatives. H matrix is calculated using formula (11): 0 1 0 0 1 1 0 1 0 H F G 0 0 0 1 0 1 0 0 0 1 1 0 1 1 0 1 1 0
With regard to matrix H in first and second column there is a minimum 1 element then it can be eliminate them, thus alternative A3 is effective to select. It can be show the relationship between alternatives based on matrix H as following: A3
A1
A2 This means that ranking the strategies based on ELECTRE method is A3 dominated A1 and A2 , A1 also dominate A2. IV.
CONCLUSION
The outranking methods are at their best in strategic level decision support. A further advantage is that preference estimation procedures of outranking methods are versatile and diverse. Generally taken, outranking methods do not require as much preference information as other MCDM methods. Easiness to use and understand the method, and interpretability of the results, are important qualities of the methods applied. In this study the results show Conservation strategy is prefer as the best management strategy for the study area. Commercial strategy is in second rank and the last one is Recreational strategy. The ranking is based on the number of alternatives outranked by each alternative minus the number of alternatives which outrank it. Fagetum communities of Hyrcanian forest are over exploited for decades and their productive potential decreases in lowland areas. In the past the forest was cutting traditionally by cut the best trees, scientific cutting from the northern forest of Iran was started by using forest plan as shelter wood system at 1961 (Poorbabaei & Ranjavar, 2008). There are socioeconomic problems as forest settlers and grazing in these forests that make damage the natural forests and over exploited there, but the fagetum community is still as commercial forests. It is essential to conserve the forest to improve the condition of them. The area is located in wide range of altitude and it is an important profit to recreation for it and the aesthetic value of the area can make it as a recreational region. The recreation strategy is as third prefer for the area.
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