IRACST – Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498 Vol.7, No.2, Mar-April 2017
ENVIRONMENTAL RANKING OF LANDFILL SITES AMONG n-ALTERNATIVES Dr. Srijit Biswas
Anjali Gupta
Professor, Department of Civil Engineering, Manav Rachna International University, Faridabad, India,
Assistant Professor, Department of Civil Engineering, Manav Rachna International University, Faridabad, India,
Abstract- In almost all cities, the solid waste generation is increased dramatically and becomes a great challenge today to municipal authority for its collection and safe disposal. Continuous uncontrolled generation of solid wastes forces the existing landfills to become exhausted rapidly[2-3]. Other hand the chance of availability of open land for future provision of landfill in a city is now very less. At present no sanitary landfill technique is practiced for disposal of wastes rather wastes are dumping crudely in a open field without any environmental protection. This indiscriminate disposal of wastes leads to significant degradation of environment and spreads diseases nearby locality of landfill site[4]. Naturally every landfill has an independent grade of acceptability to local authority in respect of their ‘Land Area’; ‘Daily loading of wastes quantity’; ‘Environmental adverse impact to nearby area’ and ‘Cost of land’ etc. In this paper a methodology of Neutrosophic Fuzzy (NSF) model has been introduced to select the environmentally best acceptable landfill out of n-alternative and make a ranking among them. To validate the model, a case study on three landfills of Delhi(capital of India) is presented here.
wide variety of hazardous chemicals as well as conventional and non-conventional contaminants that degrades the quality of original soil and underground water sources [3-4]. The sanitary condition around the periphery of landfill becomes highly polluted due to frequent interferences of birds, dogs, pigs, rodents, cows etc as well as lack of proper drainage system and unhygienic latrine. As result the whole environment become a favourable condition for high breeding of fly and mosquito and make responsible for causing different diseases like gastroenteritis, asthma, bronchitis, skin diseases, etc among local people those are living nearby landfill sites[2]. This people are often found illiterate and economically backward thus there is an essential need of continuous awareness programs on sanitation and hygienic from local authority but such practice are found in field. Due to rapid urbanization, open big field for provision of future landfill is not available easily inside the city and if available then cost of land will be very high which will not permit the local authority to purchase for purpose of wastes dumping. In other hand, existing landfills are started overflow due to uncontrolled solid wastes generation and it’s dumping in each daily. Naturally it is now necessary to an authority to assess the importance of existing all landfills and select the environmentally best acceptable landfill out of n-alternative with degree of certainty. But this type of assessment involves prediction where uncertainty has a great role for its evaluation. During assessment, data are generally found in linguistic form viz. ‘good’, ‘very good, “less amount’, ‘too much polluted’, ‘not lees than 30%’, ‘poor drainage system’, ‘many scavengers’, ‘unusual number’, ‘good ecofriendly’, ‘huge quantity’, ‘bad water quality’, ‘bad approach road’, ‘acute rodents problems’, ‘huge debris’, etc. to list a few only out of infinity. This type of data are called fuzzy data [8] and evaluation of such data are not always
Keywords: FAS, FD, SVN-set, neutrosophic logic, score function, AEIV, etc.
I. INTRODUCTION The rapid growth of population leads ultimately the increase of wastes generation in a society. Most of the solid waste management authorities are not able to collect 100% wastes from all sources of generation and dispose it safely due to their poor economical infrastructure. The disposal system is also not in sanitary manner rather drivers have usual habits to dump wastes in landfill where they felt comfortable with less time and efforts. Ultimately it creates a hillock of rubbish posing great environmental threats to nearby residential area. When moisture exceeds the field capacity of the waste matrix as a result of percolation of rain water it squeeze the waste itself and form leachate that contaminated the surrounding soil structure of landfill. It contains a
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IRACST – Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498 Vol.7, No.2, Mar-April 2017
possible with numerical valued description. Because some part of the evaluation contributes to truthness, some part contributes to falseness and the rest part remains indeterministic. Every expert or decision-maker hesitates more or less, on every evaluation activity due to their certain limitation of knowledge or intellectual functionaries and thus outcome result of their perceptions becomes with full of uncertainty [3]. The ‘Neutrosophic Fuzzy Logic’ of Prof. Florentin Smarandache [5-7] that used in this paper can give a beam of light to management authority to mitigate the adverse environmental impacts of landfill and it’s future longevity. The ‘Fuzzy Logic’ of Prof Lafti Zadeh[8] deals with truth membership function [tA(x)] within subset [0,1] and then false membership function [fA(x)] will be [fA (x) = 1- tA (x)] within sub set [0,1]. Prof. K.T.Atanassov who introduced ‘Intuitionistic Fuzzy Logic’ [1] tackled the uncertainty more precisely by generalization of normal fuzzy logic of Prof Lafti Zadeh. To tackle the uncertainty he approached a strong logic that [ tA(x) + fA(x) + iA(x) = 1], where the value of hesitation [iA(x)] or indeterministic part can estimate by [iA(x) = 1– tA(x) – fA(x)] within sub set [0,1]. Later on Prof. Florentin Smarandache introduced ‘Neutrosophic Fuzzy Logic’[5-7] which does not permit the logic of [ tA(x) + fA(x) + iA(x) = 1] within single sub set [0,1] to tackle the uncertainty at the extreme point of perception. Instead of that we have to estimate the percentage of truth in a subset T where tA(x) : x Æ[0,1], percentage of indeterminacy in a subset I, where iA(x) : x Æ[0,1], percentage of falsity in a subset F, where fA(x): xÆ[0,1] individually and independently. According to his logic there will be no restriction on summation of tA(x), iA(x) and fA(x), and the condition will exist in such that [ 0 ≤ sup tA(x) ≤ sup iA(x) ≤ sup fA(x) ≤ 3]. Thus to tackle the uncertainty with more degree of satisfaction, ‘Neutrosophic Fuzzy Logic’ is now being used in large scale in all research fields. This paper deals with a ‘Neutrosophic Fuzzy(NSF)’ model to select the environmentally best acceptable landfill out of three landfills of Delhi (capital of India). II. PRELIMINARIES Different mathematical concepts has used in this methodology which are discussed below briefly. A. Crisp Set A set can be described either by list method or by the rule method. We know that the process by which individuals from the universal
set X are determined to be either members or nonmembers of a set can be defined by a characteristic function or discrimination function. For a given set A, this function assign a value µA (x) to every x ∈ X such that µA (x)
= 1
iff x ∈ A
= 0
iff x ∉ A
Thus in the classic theory of sets, very precise bounds separate the elements that belong to a certain set form the elements outside the set. In other words, it is quite easy to determine whether an element belongs to a set or not. B. Fuzzy Set [8] Many sets encountered in reality do not have precisely defined bounds as in case of crisp sets that separate the elements in the set from those outside the set. That so the crisp characteristic function can now be generalized such that the values assigned to the elements of the universal set fall within a specified range and indicate the membership grade of these elements in the set in question. Such a function is called membership function and the set defined by it a fuzzy set. The membership function for fuzzy sets can take any value form the closed interval [0,1]. Fuzzy set A is defined as the set of ordered pairs A = { x, µA(x) }, where µA(x) is the grade of membership of element x in set A. The greater µA(x), the greater the truth of the statement that element x belongs to set A [2,7]. Let X = {x1, x2,……...., xn} be a finite discrete universe of elements xi, i = 1, 2, …. , n. A fuzzy set A defined over a set X is most often shown in the form A = {( x1, µA(x1) ) , (x2 , µA(x2) ) , (x3 , µA(x3) ) , ….. , (xn , µA(xn) ) } where µA(xi) Æ [ 0,1 ]. C. Instuitionistic Fuzzy Set [1] An intuitionistic fuzzy set (IFS) A in X is defined as an object of the following form. A = { ( x, µA (x), vA (x) ) | x є X } where the functions, µA : x Æ [ 0,1 ] and vA : x Æ [ 0,1 ] define the degree of membership and the degree of non-membership of the element x є X, respectively, and for every x є X we have the relation 0 ≤ µA(x) + vA (x) ≤ 1 which is called ‘Atanassov condition’. Obviously, each ordinary fuzzy set may be written as { ( x, µA (x), 1-µA (x) ) | x є X }
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IRACST – Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498 Vol.7, No.2, Mar-April 2017
and thus every fuzzy set is an intuitionistic fuzzy set but not conversely. The amount πA (x) = [1 - ( µA (x) + vA (x) )] is also called the hesitation part (i.e. the degree of non-determinacy or uncertainty) of the element and this amount may cater to either membership value or to nonmembership value or to both [1,5]. Clearly, in case of ordinary fuzzy sets (Zadeh’s fuzzy sets) it is presumed that πA(x) = 0 for every x є X. D. Neutrosophic Fuzzy Set (NFS) [5-7] Neutrosophic Fuzzy Set (NFS) introduced by Prof. Florentin Smarandache is now treated as super generalized set of all existing logical sets. The logic stated that truth membership function TC(x), indeterminacy function IC(x) and falsity membership function FC(x) of a NFS is to be characterized each in a 3D neutrosophic space individually within sub sets of ]-0,1+[ , where only ‘1’ and ‘0’ are the standard part and ‘ε’ its non-standard part such that 1+ = 1 + ε and -0 = 0- ε. There is no restriction on the sum of TC(x), IC(x) and FC(x) thus the relation of there membership function will be -0 ≤tA(x) + iC(x) + fC(x)≤ 3+. In neutrosophic logic, T can be split into subcomponents T1, T2, T3,…..Tp and I into I1, I2, I3, …, Ir and F into F1, F2, F3, …., Fs , and their individual membership function will be within [0,1] and to be expressed as tC(x) : x Æ [ 0,1 ] , iC(x) : x Æ [ 0,1 ] and fC(x) : x Æ [ 0,1 ] with 0 ≤ tC(x) + iC(x) + fC(x) ≤ 3 for all x є X . E. Single Valued Neutrosophic Set [7] If tA(x), iA(x) and fA(x) denote the truthmembership degree, the indeterminacymembership degree and the falsity membership degree of x of universal set X, then the single valued neutrosophic set(SVN-set) A is defined as A = {( x : tA (x) , iA(x) , fA (x) )) | x є X }, where tA(x) : x Æ[0,1] , iA(x) : x Æ[0,1] and fA(x) : x Æ[0,1] will evaluate individually so that the condition of 0 ≤ tA(x)+ iA(x) + fA(x) ≤ 3 for all x є X exists. III. METHODOLOGY To understand the functional approach of NSF model, few working tools are discussed below before starting of case study. A.
Fuzzy Alternatives Statement (FAS)
During evaluation of job, the expert’s views or local public’s opinions are often found in non-numerical or linguistic statement like ‘poor drainage system’, ‘many scavengers are working’, ‘unusual number of fly breeding’, ‘good eco-friendly’, ‘huge quantity of solid
waste’, ‘bad water quality’, ‘bad approach road’, ‘acute rodents problems’, ‘huge debris’, etc. All these data are considered as fuzzy alternatives statements (FAS) in the present model of NFS. B. Score Function Fuzzy Sets If A is a single valued neutrosophic set and tA(x) , iA(x) and fA(x) are suppose degree of truthness, degree of indeterminacy and degree of falsity, then score function fuzzy set SF(x) is defined by the membership function (SF) as SA(x) =
[tA(x) + iA(x)/2] + [1- {fA(x) + iA(x)/2}] 2
where, set-SA(x) Æ [-1, 1] and tA(x)+ iA(x) + fA(x) ≤ 3 for all x є X. C. Average Environmental Impact Value (AEIV) If the set-SL(x) is a score function fuzzy sets such that for each element x ∈X, there is an associated weight Wi ∈ R+ (which could be prefixed by the common decision of all experts before commencement of case study), then the ‘Average Environmental Impact Value (AEIV)’ of the score function fuzzy sets is the nonnegative number a(x) given by : ∑SL(xi).WXi a(x) = ∑WXi D. Fuzzy Decision ( FD) In every evaluation activity our target is always to achieve the goal where many constraints are clubbed with all possible decisions. Due to lack of knowledge or limitation of intellectual functionaries a decision maker hesitates more or less for taking perfect decision facing all constraints that plays an integral part for achieving the targeted goal. The fuzzy decision (FD) is an appropriate tool to achieve the targeted goal minimizing the doubt and uncertainty that arises in the perception of the experts or decision makers for solution this type of problems. To understand the function of FD an algorithm is presented below. Algorithm of FD : Here we use the general concept of fuzzy logic of Prof. Lafti Zadeh where the membership value (µ) for the ‘maximum favourable condition’ of an option is considered as 1 and for ‘not maximum favourable condition’ it is 0. Let us consider a group of options as O
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IRACST – Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498 Vol.7, No.2, Mar-April 2017
Where, O = { O1, O2, O3, ……., OL} = { Oi }, for i = 1, 2, 3, …, L Let a fuzzy set G describing goals associated with each option (oi) such that G = { µ(g1/o1), µ (g2/o2), µ (g3/o3), ……., µ (gL/oL)} = {µ(gi/oi) }, for i = 1, 2, 3, …, L
(Fig1-Different Scenario of Okhla landfill)
Now if the three fuzzy sets C1, C2 and C3 describing three constraints associated with each option (oi) such that C1= { µ1(c1/o1), µ1(c2/o2), µ1(c3/o3), ……., µ1(cL/oL) } = { µ1(ci/oi)}, for i = 1, 2, 3, …, L And C2 = { µ2(c1/o1), µ2(c2/o2), µ2(c3/o3), ……., µ2(cL/oL) = { µ2(ci/oi)}, for i = 1, 2, 3, …, L
(Fig2-Different Scenario of Bhalaswa landfill)
And C3 = { µ3(c1/o1), µ3(c2/o2), µ3(c3/o3), ……., µ3(cL/oL) } = { µ3(ci/oi)}, for i = 1, 2, 3, …, L Then the Fuzzy Decision (FD) will be given by FD = Max {D(oi)} , where D(Oi) = [ sub set-G ∩ sub set-C1 ∩ subset-C2 ∩ subset-C3 ] = Min {µ( gi/oi), µ1( ci/oi), µ2( ci/oi), µ3( ci/oi)} Now to validate the fuzzy model, a case study is presented below. IV. CASE-STUDY To validate the NSF model we consider a project ‘Environmental ranking of three landfill among ‘Okhla at South Delhi; Bhalaswa at North Delhi and Ghazipur at East Delhi’. The land area of Okhla is 16.20 Ha started from 1992 and wastes received per day 1200 tons covering the area of Central Delhi, South Delhi, Najafgarh and Cantonment area. The land area of Bhalaswa is 21.06Ha started from 1993 and wastes received per day 2200 tons covering the area of Civil Line, Karol Bagh, Rohini, West Delhi and Najafgarh while the area of Ghazipur is 29.16 Ha started from 1984 and wastes received per day 2000 tons covering the area of Shahdara (North), Shahdara(South), Sadar Paharganj & NDMC area.
(Fig3-Different Scenario of Ghazipur landfill) For simplicity in presenting the methodology we selected ten locations around the periphery of each landfill and thirty experts who would finalize twenty five fuzzy alternative statements (FASs) and their individual weight (WXi). Then we grouped ten experts for obtaining their views in favour of truth-membership value [t(x)], ten experts for obtaining their views in favour of indeterminacy-membership value [i(x)] and rest ten experts for obtaining their views in favour of falsity membership value [f(x)] during evaluation of all FASs. Suppose the twenty five FASs that considered by the thirty experts for in case study are:x1 = many vehicles are plying daily for disposal of wastes x2 = poor drainage system around the landfill site x3 = many scavengers are working in the landfill site x4 = unusual number of mosquito breeding in landfill site
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IRACST – Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498 Vol.7, No.2, Mar-April 2017
x5 = unusual number of fly breeding in landfill site x6 = unhygienic latrine in and around the landfill site x7 = acute birds problems in landfill site x8 = acute rodents problems in landfill site x9 = poor mechanical condition of vehicles
carrying
x10 = inadequate water facilities in landfill site x11 = heavy rainfall intensity in landfill site area x12 = poor management for disposal of waste timely x13 = bad habit of neighbors roaming around the disposal site x14 = poor awareness of sanitation among the neighbors x15 = poor awareness of sanitation among the scavengers x16 = easy accessibility of dogs, pigs, cows, etc. in the landfill site x17 = very crude dumping system of solid waste x18 = heavy production of vegetables & fishes around the landfill area x19 = poor barricade in between dumping area and neighbor x20 = bad habit to use the recyclable materials by the neighbors x21 = high mixing habit of scavengers and neighbors x22 = bad approach road around the landfill site x23 = huge quantity of solid waste dumping daily x24 = bad water quality x25 = huge amount of leachate coming openly from landfill periphery These data leads to a neutrosophic fuzzy set of universe X, where X = {x1, x2, x3, x4, x5, ………….., x23, x24, x25 }. Suppose the individual weight (out of 10) of each FAS prefixed by the joint decision of thirty experts are: WXi ={ x1 = 2, x2 = 8 , x3 = 2 , x4 = 7, x5 = 7, x6 = 9, x7 = 4, x8 = 6, x9 = 3, x10 = 8 , x11 = 10 , x12 = 7, x13 = 5 , x14 = 4, x15 = 3, x16 = 7 , x17 = 6, x18 = 2 , x19 = 10, x20 = 7, x21 = 5, x22 = 7, x23 = 10, x24 = 9, x25 = 10}.
Now the job is to assign values of these FASs by the three individual groups of experts independently for ten locations of each landfill for assessment of their individual AEIV. Assessment of AEIV of Okhla Landfill : Suppose for location-1, the SVN set-L1 is assigned as below:SVN set-L1 = {(x1,0.7,0.6,0.1), (x2,0.5,0.6,0.2), (x3,0.4,0.2,0.6), (x4,0.8,0.1,0.2), (x5,0.5,0.2,0.3), (x6,0.5,0.5,0.1), (x7,0.7,0.3,0.3), (x8,0.7,0.6,0.1), (x9,0.5,0.6,0.2),(x10,0.6,0.2,0.4), (x11,0.5,0.2,0.4), (x12,0.2,0.7,0.4),(x13,0.2,0.3,0.5), (x14,0.2,0.6,0.2), (x15,0.5,0.6,0.2),(x16,0.8,0.1,0.1), (x17,0.6,0.4,0.2), (x18,0.5,0.4,0.2),(x19,0.5,0.3,0.2), (x20,0.4,0.6,0.2) (x21,0.7,0.3,0.2),(x22,0.8,0.2,0.1), (x23,0.5,0.5,0.3), (x24,0.5,0.3,0.4), (x25,0.7,0.1,0.2)}. The Score Function fuzzy set SL(x) of above SVN set-L1 is thus calculated as SL1(x)= {(x1,0.80), (x2,0.65), (x3,0.40), (x4,0.80), (x5,0.60), (x6,0.70), (x7,0.70), (x8,0.80), (x9,0.65), (x10,0.60), (x11,0.55), (x12,0.40), (x13,0.35), (x14,0.50), (x15,0.65), (x16,0.85), (x17,0.70), (x18,0.65), (x19,0.65), (x20,0.60), (x21,0.75), (x22,0.85), (x23,0.60), (x24,0.55) , (x25,0.75)}. Similarly for the rest nine locations of Okhla landfill periphery, the assigned score function fuzzy sets are suppose as follows: SL2(x) = {(x1,0.05), (x2,0.45), (x3,0.25), (x4,0.30), (x5,0.40), (x6,0.20), (x7,0.60), (x8,0.30), (x9,0.20), (x10,0.45), (x11,0.15), (x12,0.60), (x13,0.30), (x14,0.25), (x15,0.25), (x16,0.55), (x17,0.25), (x18,0.25), (x19,0.50), (x20,0.30), (x21,0.45), (x22,0.75,), (x23,0.35), (x24,0.55), (x25,0.65)} SL3(x) = {(x1,0.35), (x2,0.15), (x3,0.35), (x4,0.50), (x5,0.25), (x6,0.40), (x7,0.20), (x8,0.30), (x9,0.20), (x10,0.65), (x11,0.05), (x12,0.30), (x13,0.10), (x14,0.25), (x15,0.25), (x16,0.55), (x17,0.50), (x18,0.55), (x19,0.40), (x20,0.10), (x21,0.45), (x22,0.25), (x23,0.65), (x24,0.40), (x25,0.20)} SL4(x) = {(x1,0.35), (x2,0.55), (x3,0.50), (x4,0.40), (x5,0.25), (x6,0.45), (x7,0.60), (x8,0.60), (x9,0.30), (x10,0.25), (x11,0.25), (x12,0.10), (x13,0.50), (x14,0.55), (x15,40), (x16,0.55), (x17,0.50), (x18,0.20), (x19,0.20), (x20,0.25), (x21,0.65), (x22,0.25,), (x23,0.35), (x24,0.15), (x25,0.45)} SL5(x) = {(x1,0.25), (x2,0.35), (x3,0.55), (x4,0.65), (x5,0.45), (x6,0.80), (x7,0.20), (x8,0.20), (x9,0.60), (x10,0.65), (x11,0.75), (x12,0.50), (x13,0.30), (x14,0.95), (x15,0.45), (x16,0.45), (x17,0.80), (x18,0.65), (x19,0.40), (x20,0.60), (x21,0.30), (x22,0.50,), (x23,0.65), (x24,0.70), (x25,0.85} SL6(x) = {(x1,0.65), (x2,0.65), (x3,0.25), (x4,0.50), (x5,0.85), (x6,0.90), (x7,0.20), (x8,0.10), (x9,0.60),
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IRACST – Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498 Vol.7, No.2, Mar-April 2017
(x10,0.65), (x11,0.25), (x12,0.40), (x13,0.10), (x14,0.15), (x15,0.45), (x16,0.95), (x17,0.60), (x18,0.20), (x19,0.45), (x20,0.35), (x21,0.65), (x22,0.35,), (x23,0.25), (x24,0.20), (x25,0.40)} SL7(x) = {(x1,0.75), (x2,0.35), (x3,0.25), (x4,0.30), (x5,0.25), (x6,0.60), (x7,0.20), (x8,0.80), (x9,0.10), (x10,0.65), (x11,0.75), (x12,0.30), (x13,0.10), (x14,0.25), (x15,0.60), (x16,0.75), (x17,0.20), (x18,0.35), (x19,0.20), (x20,0.20), (x21,0.15), (x22,0.55,), (x23,0.45), (x24,0.05), (x25,0.20)} SL8(x) = {(x1,0.85), (x2,0.35), (x3,0.65), (x4,0.70), (x5,0.55), (x6,0.80), (x7,0.40), (x8,0.40), (x9,0.20), (x10,0.25), (x11,0.15), (x12,0.10), (x13,0.40), (x14,0.25), (x15,0.40), (x16,0.70), (x17,0.50), (x18,0.20), (x19,0.70), (x20,0.20), (x21,0.65), (x22,0.45,), (x23,0.05), (x24,0.45), (x25,0.20)} SL9(x) = {(x1,0.25), (x2,0.35), (x3,0.05), (x4,0.70), (x5,0.65), (x6,0.40), (x7,0.20), (x8,0.30), (x9,0.20), (x10,0.65), (x11,0.05), (x12,0.30), (x13,0.10), (x14,0.25), (x15,0.45), (x16,0.75), (x17,0.20), (x18,0.25), (x19,0.40), (x20,0.20), (x21,0.35), (x22,0.45,), (x23,0.05), (x24,0.10), (x25,0.40)} SL10(x)= {(x1,0.05), (x2,0.35), (x3,0.05), (x4,0.70), (x5,0.65), (x6,0.40), (x7,0.20), (x8,0.30), (x9,0.20), (x10,0.65), (x11,0.05), (x12,0.30), (x13,0.10), (x14,0.25), (x15,0.65), (x16,0.75), (x17,0.20), (x18,0.25), (x19,0.40), (x20,0.20), (x21,0.35), (x22,0.45,), (x23,0.05), (x24,0.10), (x25,0.40)]. Now, mean of above ten score function fuzzy sets is also a new score function fuzzy set SL(X), which could be as below. SL(X) = {(x1,0.39), (x4,0.555), (x5,0.505), (x8,0.40), (x9,0.34), (x12,0.33) ,(x13,0.25), (x16,0.685), (x17,0.415), (x20,0.32), (x21,0.455), (x24,0.33), (x25,0.435)}
(x2,0.42), (x3,0.355), (x6,0.555), (x7,0.35), (x10,0.57), (x11,0.31), (x14,0.37), (x15,0.45), (x18,0.355) ,(x19,0.435), (x22,0.475), (x23,0.35),
case study cost of land is considered as per local market rate which has no influence in expert’s perception directly. Landfill (LF)
∑SL(xi).WX ∑WXi
=
Daily loading of wastes quantity (In tons)
AEIV
Cost of Land (Rs.in lakhs)
Okhla Landfill (LF1)
16.20
1200
425
230
Balawasr Landfill (LF2)
21.06
2200
439
120
Gazipur Landfill (LF3)
29.16
2000
255
150
A landfill (LF) will be more acceptable when more land area is available to management for disposal of wastes for long period. Thus in the present NSF-model ‘Maximum land area’ is considered as our goal, i.e. G and other three options: ‘Maximum daily loading of wastes’ ; ‘Maximum AEIV’ and ‘Maximum cost of land’ are considered as three constraints, i.e. C1, C2 and C3 respectively. Now according the tool of FD, the fuzzy sets for each option are assessed as: G = µ(gi/LFi) = [ 0.6/LF1, 0.90/LF2, 1.0/LF3] C1 = µ(C1/LFi) = [ 1.0/LF1, 0.70/LF2, 0.50/LF3] C2 = µ(C2/LFi) = [ 0.70/LF1, 0.65/LF2, 1.0/LF3] C3 = µ(C3/LFi) = [ 0.40/LF1, 1.0/LF2, 0.70/LF3] Therefore, D(LFi) = µ(g/LFi) ∩ µ(C1/LFi) ∩ µ(C2/LFi) ∩ µ(C3/LFi)
Therefore AEIV of Okhla landfill a(x) =
Land Area (In Ha)
= [0.40/LF1, 0.65/LF2, 0.50/LF3] 67.14
= 0.425
158
Similarly the AEIV for the landfill of Balawasr and Gazipur are calculated as 0.439 and 0.255. Now the job is to take decision which landfill out of three is more environmentally favourable to solid waste management authority using Fuzzy Decision (FD) tool. Here FD tool is applied considering final crisp data about ‘land area’, ‘daily loading of wastes quantity’, ‘AEIV’ and ‘cost of land’ and then finally come into conclusion which one is actually most environmentally best out of three landfills. In this
Then the fuzzy decision is given by FD
= 0.65/LF2 > 0.50/LF3 > 0.40/LF1
Thus result reveals that the landfill ‘Balawasr Landfill’ is the best environmental suitable landfill out of three landfills of Delhi NCR. The next is Gazipur Landfill and worst is Okhla Landfill. CONCLUSION At present there is no tool available to a solid waste management authority which can solve the problem of uncertainties that faced by a decision maker for selection of best landfill out of n-alternatives options. In each landfill there are
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IRACST – Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498 Vol.7, No.2, Mar-April 2017
multiple environmental problems as well as importance too in respect of availability of land area for its future extension and grade of unhygienic loading of wastes in it. This NSF model will give a light of beam to authority to take decision more precisely which landfill is actually so much acceptable environmentally out of many under his control or supervision. This type of fuzzy model will also help the authority to make ranking all landfill and can take precaution and alertness for it’s future growth and life keeping safety in all respect of environmental issues.
journals and published 54 research papers. He authored a book on the broad area of ‘Fuzzy EIA’ published from Germany. Ms. Anjali Gupta: She did M.Tech from NIT Kurukshetra and now also pursuing Ph.D from NIT Kurukshetra. Currently she is working as Assisstant Professor, Department of Civil Engineering, Manav Rachna International University, Faridabad. She has 10 years of teaching experience and published 3 research papers.
REFERENCES [1] Atanassov.K.T.; ‘Instuitionistic Fuzzy Sets’, Fuzzy Sets and System, vol-20, no-1, pp 87- 96 (1986). [2] Biswas. Srijit; Sonal Bhugra; ‘Assessment of Health Hazardous of Scavengers Using Intelligent System”; “International Journal of Emerging Engineering Research and Technology (IJEERT)”, Volume 3, Issue 9: 41-44 (Sept’2015). [3]. Srijit Biswas, Sonal Bhugra : “Assessment of Sanitary Condition Nearby Area of Solid Wastes Disposal Site”; “International Journal of Scientific Research in Science Engineering & Technology (IJSRSET)”, Volume 1, Issue 5: 218-224-719 (Oct’2015). [4] Omar Al-Jarrah, Hani Abu-Qdais, ‘Municipal solid waste landfill sitting using intelligent system’, Elsevier-Waste Management, vol-26, pp 299-306 (2006). [5] Smarandache, F., ‘An unifying field in logics, Neutrosophy: Neutrosophic Probability, set and logic’, American Research Press, Rehoboth(1999). [6] Smarandache, F., ‘A generalization of the Instuitionistic Fuzzy Set’, International Journal of Pure and Applied Mathematics, vol-24, pp 287-297(2005). [7] Wang. H.; Smarandache, F.; Zhang, Y. Q., and Sunderrman. R.(2010); Single valued neutrosophic sets, Multispace and Multistructure, vol-4, pp-410-413 [8] Zadeh. L.A, ; ‘Fuzzy Sets: Information and Control’, Vol-8, no-3, pp-338-353.(1965).
AUTHORS PROFILE Dr. Srijit Biswas: He did Ph.D from Jadavpur University and BE & ME from Calcutta University. He is ‘Fellow’ of Institute of Engineers (India) and working as a Professor, Department of Civil Engineering, Manav Rachna International University, Faridabad. He has more than 29 years experiences. He is the member of editorial board of many reputed international
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