Iranian J Env Health Sci Eng, 2004, Vol.1, No. 2, pp.16-27 Mohammad Karamou et al: River Water Quality…

River Water Quality Zoning: A Case Study of Karoon and System

*M Karamouz 1, N Mahjouri 2, R Kerachian 3

1School of Civil and Environmental Engineering, Amirkabir University, 2 Faculty of Environmental Engineering, University of , Iran 3Dept. of Civil Engineering, University of Tehran, Iran

ABSTRACT

Karoon-Dez River basin, with an area of 67000 square kilometers, is located in southern part of Iran. This river system supplies the water demands of 16 cities, several villages, thousands hectares of agricultural lands, and several hydropower plants. The increasing water demands at the project development stage including agricultural networks, fish hatchery projects, and inter-basin water transfers, have caused a gloomy future for water quality of the Karoon and Dez Rivers. A good part of used agricultural water, which is about 8040 million cubic meters, is returned to the rivers through agricultural drainage systems or as non-point, return flows. River water quality zoning could provide essential information for developing river water quality management policies. In this paper, a methodology is presented for this purpose using methods of c -mean crisp classification and a fuzzy clustering scheme. The efficiency of these clustering methods was evaluated using water quality data gathered from the monitoring sampling points along Karoon and Dez Rivers. The results show that the proposed methodology can provide valuable information to support decision-making and to help river water quality management in the region.

Keywords: Fuzzy Clustering, Data classification, Water quality zoning, River systems, Karoon, Dez

INTRODUCTION water quality control projects. Horton (1965) proposed the first Water Quality There are many similarities between certain Index (WQI). The critical issue in water quality phenomena in the nature. Quantitatively inter- zoning is to construct WQI in a simple way so preting these similarities can make it possible to that the correct information can be extracted categorize the components of a system into dif- from measurements in order to illustrate the ferent classes based on a pre-defined criterion. special and temporal variations (Ott, 1980). One of the main applications of the “clustering Simple weighing method, which is usually used methods” is zoning the river systems based on for water quality zoning, depends on expert various water quality variables. River quality judgments and therefore has been greatly influ- zoning is important in determining the critical enced by human subjectively, even though all zones, optimal locations of the monitoring sta- parameters are carefully measured. In addition, tions and water intakes as well as proposing technical interpretation of WQIs is usually dif- ficult and indexing methods have some weak- *Corresponding author: E-mail: [email protected] Tel: +98 21 664543014, Fax: +98 21 66414213, nesses in explaining a complicated situation. In

16 Mohammad Karamouz et al: River Water Quality…

some cases, an expert should check the con- METHODOLOGY centration of all of the water quality variables In this paper, the efficiency of the two well- for water quality explanation and it signifi- known classification methods, namely hard cantly reduces the value of the index methods. c-mean crisp classification and fuzzy clustering Most of the traditional tools for formal model- are evaluated. ing and computing are crisp, deterministic and Hard c-means (HCM) precise in characteristics. Zadeh (1965, 1973) HCM is used to classify data in a crisp sense. In stated that real situations are very often this method, each data point will be assigned to uncertain or vague in a number of ways; he one, and only one, data cluster. A family of sets called this vagueness "fuzziness" and proposed {Ai ,i = 1,2,Lc} as a hard c -partition of set X the Fuzzy Sets Theory. The following constraints After that, many theoretical developments in should be defined. fuzzy logic took place. Now, fuzzy logic affects are applied to these partitions: many disciplines in different fields of study.

There is an underlying structure to be under- A1 U A2 ...U Ac = X (1) stood. We often seek to find structure in the A A = φ ∀i ≠ j (2) data obtained from observation such as water i I j quality data provided by a monitoring system. φ ⊂ Ai ⊂ X ∀i (3) Finding the data structure is the essence of clas- 2 ≤ c < n (4) sification. In data classification, also termed clustering, the most important issue is deciding Where X = {x ,x ,x ,...,x } is a finite set, what criteria to classify against it. In water 1 2 3 n quality zoning, deviation from water quality comprised of the of data samples, and c is the standard is usually considered as the clustering number of partitions or clusters, in which the criteria. data will be classified. Where c = n , classes Classical (crisp) clustering algorithms generate just places each data sample into its own class, different clusters so that each object is assigned and c = 1 places all data samples into the same to exactly one cluster. However, in many cases, class; neither case requires any effort in classi- objects cannot adequately be assigned to strictly fication and both are intrinsically uninteresting. one cluster because they are located between Equation 1 expresses the fact that the set of all clusters. In such a case, fuzzy clustering classes exhaust the universe of data samples. method may provide clusters that are more re- Equation 2 indicates that none of the classes alistic by assigning overlapping membership overlaps in the sense that a data sample can functions. Kung et al. (1992) proposed a water belong to more than one class. Equation 3 quality index for water quality assessment and simply expresses that a class cannot be empty showed that fuzzy clustering analysis might be and it cannot contain all the data samples. used as a complement or an alternative to Within all the possible c -partitions for n data common quantitative methods in water quality samples, the most reasonable c partition should assessment, especially when WQI scores are be selected. A within-class sum of squared er- close to the thresholds between normal and ab- rors approach using Euclidean norm to charac- normal condition. In this paper, the main char- terize distance, is used in c -mean clustering. In acteristics of crisp and fuzzy clustering methods this approach, Euclidean distance is measured th th and their suitability for river water quality between the k data sample xk and i cluster zoning are presented and the efficiency of the- center v~ by the following expression (Ross, ses methods are evaluated using water quality i 1997): data of Karoon-Dez River system.

17 Iranian J Env Health Sci Eng, 2004, Vol.1, No. 2, pp.16-27

0.5 m Symmetry µR(xi ,xj )=µR(xj ,xi ) (11) ~  2  d ik = xk − vi = ∑ ()xkj − vij  (5)  j=1  Transitivity

Since each data sample requires m coordinates µR (xi , x j ) = λ1 and µR (x j, xk ) = λ2 → µR (xi , xk ) = to describe its location in m dimensional space, λ where λ ≥ min[λ ,λ ] each cluster center also requires m coordinates 1 2 to describe its location in this same place. th Therefore, the i cluster center is a vector of (12) length m as:

In which, µ R (xi ,x j ) is the membership func- tion of the fuzzy relation R , which shows the ~v = {v ,v ,...,v } (6) i i1 i2 im similarity of, x and x . When there are several i j criteria in evaluating the similarity of some Where the j th coordinate is calculated by: elements, fuzzy relation can be presented by a similarity matrix. Many investigators have pro-

n posed several similarity matrices. For example, χ .x ∑k =1 ik kj Wang (1983) proposed the following similarity v = (7) ij n matrix: χ ∑k =1 ik

1 x j ∈Ai  p  χij =  (8) ∑ (x .x ) 0 x ∉A  ik jk   j i  k =1 p  rij = +  i ≠ j  p p p p  ∑ (x .x ). ∑ (x .x ) ∑ (x .x )− ∑ (x .x )  ik ik jk ik ik ik jk ik  The optimum partitions that are selected pro- k =1 k =1 i =1 i =1  duce the minimum value for the following (13) objective function.

n c 2 rij = M ≥ Max (rij ∀i ≠ j) i ≠ j (14) Min z = ∑∑χ ik ()d ik (9) k ==1 i 1 th Where, rij is ij element of the similarity Fuzzy clustering using equivalence relation. matrix, x is the value of k th criterion for In fuzzy clustering, to classify data points using ik fuzzy relations, we need to find the associated element i , and p is equal to the number of fuzzy equivalence relation. A fuzzy relation, on criteria such as water quality indicators. a single set X is also a relation from X to X . According to this formula, the value of the It is a fuzzy equivalence relation if all of the diagonal of the new matrix should be greater following three properties for matrix relation than the greatest element of the matrix. can be applied: In most real cases, the fuzzy relation being established is not stabilized; it meets the

Reflexivity µ R (xi ,xi ) 1= (10) reflexivity and symmetry requirements, but not the transitivity. Thus, the fuzzy relation has to

18 Mohammad Karamouz et al: River Water Quality…

be transformed before it can become part of a Based on different values of λ cuts, we can clustering chart. This transformation can be classify the data as illustrated in Table 1 and done through a Max-Min self-multiply Fig 1. processing of fuzzy matrix that is: The main five steps in river water quality zon- ing using fuzzy clustering analysis are as fol- rij = V( rik Λ rjk ) (15) lows: a) Selection the clustering criteria Where V is the maximum operator and Λ is the In river water quality zoning, the clustering minimum operator; rij represents the element of criteria are usually the concentration of indica- th th the i row and the j column in the similarity tor water quality variables. These indicator measure matrix R and can be defined as variables and therefore the water quality zon- follows: ing, usually depend on the beneficial uses of the

m surface water. b) Standardizing of the water quality data ∑ min()xik ,x jk r = k =1 The available data related to the concentration ij m of water quality variables should be standard- max x ,x ∑ ()ik jk ized to make them compatible. To standardize k =1 i , j = 1,2 ,..., n (16) the concentration of the water quality pollutant such as TDS, the measured concentration is di- After several steps, the fuzzy relation converges vided by the corresponding standard value. For to R* which is a stabilized relation (Wang, some water quality variables such as DO, which 1983): a higher concentration shows a better water quality condition, the observed concentration is * k 2k R = R = R (17) standardized by dividing the measured k th concentration by the related standard. Where, R is the similarity matrix after K c) Determination of the similarity matrix Max-Min self-multiplying process. In spite of Equations 13 and 14 can be used to develop a non-fuzzy relations that their elements can be similarity matrix. In these equations, n is the only 0 and 1, fuzzy relation elements can have number of monitoring stations, xik is a standard the quantities between 0 and 1. For the data concentration and p is equal to the number of classification with the equivalence fuzzy rela- indicator water quality variables. tions, it is necessary to find the stabilized d) Matrix Stabilizing equivalence fuzzy relations. Then the data The similarity matrix should be stabilized using based on different λ cuts should be classified. Max-Min self-multiplying process. For example, assuming that the matrix R is an e) Determining the clusters stabilized equivalence fuzzy relation, applying In this step, considering the objectives of water the λ cuts 0.9, we have: quality zoning, the clusters are determined

1 0 0 0 0 based on different values of λ cuts.  1 .8 .4 .5 .8 The water quality zoning was applied to Ka-   0 1 0 0 1 .8 1 .4 .5 .9   roon and Dez Rivers water quality data using R = 0 0 1 0 0 R = .4 .4 1 .4 .4 0.9 the crisp and fuzzy clustering methods.     .5 .5 .4 1 .5 0 0 0 1 0 .8 .9 .4 .5 1  0 1 0 0 1

19 Iranian J Env Health Sci Eng, 2004, Vol.1, No. 2, pp.16-27

tilizers, heavy metals, suspended and dissolved λ=0.9 λ=0.8 λ=0.5 λ=0.4 solids and pesticides, which violate from the x2 national effluent standard. Agricultural and x agro-industrial return flows, domestic waste- 5 water of the cities and villages and industrial x1 effluents are the main pollution point sources of x the surface and groundwater resources in the 4 Karoon and Dez River basins. Fig. 2 illustrates x the location of the main agricultural, industrial 3 and domestic pollution sources in the study Fig. 1: Clustering diagram and the concept of λ cuts area. Additionally, there are some diffused for fuzzy relation R pollution sources, which are not so visible, but could also be significant. The Karoon-Dez river Table 1: Clustering of 5 points based on fuzzy relation system supplies the water demands of 16 cities, R and on different quantities of λ cuts several villages, thousands hectares of agricultural lands, and several hydropower Clustering results λ plants. Increasing water demands at the devel- opment stage including agricultural networks,

{x 1 }{x 2 }{x 3 }{x 4 }{x 5 } 1 fish hatchery projects, and inter-basin water transfers, could result in a gloomy future for {x }{x , x }{x }{x } 0.9 1 2 5 3 4 water quality of the Karoon and Dez Rivers. In

{x 1 , x 2 , x 5 }{x 3 }{x 4 } 0.8 such a system, water quality zoning is needed for better river water quality management. x , x , x , x x 0.5 { 1 2 4 5 }{ 3 }

{x 1 , x 2 , x 3 , x 4 , x 5 } 0.4

CASE STUDY

The Karoon-Dez River basin, with an area of 67000 square kilometers, is located in southern part of Iran. Karoon River water pollution due to increasing water withdrawals from and wastewater discharges into this river has en- dangered the aquatic life of the river. Further- more, the drinking and in-stream water quality standards have been violated in many instances. Water pollution of Karoon River system can significantly affect the development of as one of the strategic provinces of the country with a high potential for agricultural and industrial development. A good part of used agricultural water is returned to the rivers by drainage and return flows. The return flows have a high concentration of fer-

20 Mohammad Karamouz et al: River Water Quality… Important agriculture and Factories IN A agroindustrial dischargers

A1= Saghari Drainage IN1= Sugar Factory A2=Salyme and Ajibrood Dranage IN2=Pars Paper Factory IN3=Karoon Wood Factory A3=Haft-Tappe Sugare Cane Drainage IN4=RaminThermal Plant Factor A4=AtijDrainage A5=Shooshtar Sugare Cane Drainage IN5=Zargan- Thermal Plant Factor IlamProvince A6=Shoaybie Sugare Cane Drainage IN6=Ahvaz Khoramnoosh Factory A7=Aghili Drainage IN7=Sepanta A8=Karoon Sugare Cane Drainage (Shotayt) IN8=Khuzestan Pipe Factory A9=Local Agricultural Drainages IN9=Ahvaz Pipe Factory IN10=Ahvaz National Steel Group A10=Mola-Sani Drainage Dez IN11=Ahvaz Slatter House Andymeshk A11=Karoon Sugare Cane Drainage (Dez-Hafttappe) Dez irrigation IN12=Farsit n i s A12=Dimche-Zoorabad Drainage network a IN13=Pasargad Chemical Factory IN32 B A13=Janatmakan (1,2) Drainage r IN14=Khoramshahr Soap Factory e v Lali

Dezful i A14=Kharoor drainage (Dez) IN15=Khoramshahr Khoramnoosh Factory

r R

e IN64 Dosalegh v

i IN37 z IN16=Abadan Petrochemical Factory R e

IN33

Agricultural z

e D D IN17=Abadan Refinery N Zone A1 G o IN68 e t A K v y g a a IN60 IN46 IN23 h n r s i o d IN18=Abadan MilkFactory l h i o i i n e r r r r R i IN35 k i Shoosh g g i a a a A12 v IN43 t ti e r i IN19=Ahvaz MilkFactory Fakeh Agricultural o o r IN1 e n n e n K t e w Zone t w o IN2 a A13 r o k r IN20=ZamZam r o k A7

o

Fakeh A3 n

A11 a IN21=Ahvaz Niknoosh Factory

g Haft-Tappe A2 r IN34 IN38 o

Ajirob i IN22=Khoramshahr Niknoosh Factory n Shahid-Abaspoor dam d A8 E u i r a s IN23=Gotvand Slatter House D r s t IN25 i t Arayz Agricultural Zone r M e g K y v a o i e t f a i IN24=Haft-Tappe Sugar Cane Factory a l o G r n o k n a p a h b IN56 e m p r A4 - A9 g IN25=Karoon Sugar Cane Factory h r S a S e o R h r h n j i ( o o t e v D a Bagheh Mianab c IN26=Pisciculture Drainage Bostan a y o p e n b t r A5 y i s r a e h o l h Agricultural ) t j a e IN27=Ahvaz Sugar Factory r c Eeze Zone t o f IN28=Abadan Dej-Shimi Company Iraq K ark IN29=Mola-Sani Animal Husbandry he hR A6 Naftsefid

ive K IN30=Ahvaz Paper Factory

r o A10

s

a r

Dehkhoda N Mola Sani IN31=Lastik Company (south of Abadan)

P IN54

o

r

r o

Sugar Cane t HaftGol

IN4 h

j

-

e r BaghMalek E

c e IN32= Slatter House a

t IN29

s v t i

o R Vays city f

nA IN33=Dezful Slatter House

o h

o v r a IN30 a z

IN27 p IN34=Shooshtar Slatter House

r

K o j

n e

IN5 c i t IN20 IN55 s IN6 IN35=Shoosh Slatter House a B IN10 IN57 IN19 r IN8 IN9 IN7 IN36= Shirin Kulak Company e IN21 v i IN12 IN11 IN37= Khamirmayh khuzestan Company R h IN38=Khurak dam karoon Company e Vaes h IN30 k A14 IN27 IN43= Harir Khuzestan Company r Developing a Khazaee K Agricultural IN5 IN45= Khoramshahr Fakhr Company

Zone South of IN20 IN6

F IN46=Khuzestan Shayan Company a

r Karkheh

Amirkabir Ghazali a

b IN10 i IN19 IN47=Pakineh Shu Company IN8 IN9 IN7 Mi rza IN21 IN49= Iran Marine strucure Company Koochak Khan IN12 IN11 IN51= Imam Khomeini Company IN Industrial Center IN52= Abadan Polyethylene Pipe Company in ova rkh A Agricultural drainage IN54= South civilization Company JofayrIrrigation Da r Network e Pisciculture Centers iv IN55= Galk Company R n o IN56= Karoon Cement Company o Agroindustry r a K IN13 IN56= Khuzestan Cement Company IN18 IN59= Siypa Company

IN14 IN59 IN49 IN60= Khuzestan Motorcycle Company IN47 IN69 IN22 IN61= Shipbuilding Company IN15 IN66 IN61

IN67 IN63= Arjan Novin Company Khoramshahr IN45 IN28 IN52 IN64= Dezful Tafteh Company IN16 IN66= Anousheh Gohar can Company Abadan IN17

IN31 IN67=Abadan Aluminum Oxide Company IN68=Khuzestan Pegah Milk Factory Arvand Kenar IN69= Khoramshahr Yas Company AbadanandKhoramshahrirrigationproject

AbadanandKhoramshahrirrigationproject

Fig. 2: The main components of the system in the Karoon-Dez River Basin

21 Iranian J Env Health Sci Eng, 2004, Vol.1, No. 2, pp.16-27

Dez Dam Andimeshk City Chamgolak

D e z R i v Gotvand e r

Shooshtar City

Band e Mizan S Abbaspour Dam h o t Pol e Koshtargah a G i a t r R g i a v r e r R i v e r

Band e Ghir Mollasini

Veis City

Zargan Ahvaz City Pol e Panjom

Om el Tomair Legend N

Constructed dam Underconstruction dam River Darkhovain City r e iv R Hatchery n o Drain o r a Lake K

B a Relatively High City h m High an Abadan City sh Intermediate ir A R r i Low v ve an r d R Very Low i ve r Affected by the

Persian Gulf

Fig. 3: River water quality zoning using fuzzy clustering method considering the river water quality standard ( λ = 12.5 )

22 Mohammad Karamouz et al: River Water Quality…

RESULTS the selected water quality variables and the river water quality standard. Water quality data of the sampling points of Karoon Water Quality Zoning Using Fuzzy Khuzestan Department of Environment and Clustering Method The main steps of water Khuzestan Water and Power Authority during quality zoning using fuzzy clustering method the years of 1992-2001 are used. The data defi- were presented in the methodology section. For ciencies are tackled using the correlation be- calculating the similarity matrix elements, tween water quality variables as well as engi- equations 13 and 14 were used. The number of neering judgment. the reaches related to the quality zoning is equal Karoon Water Quality Zoning Using C-Mean to 11. Therefore, the resulting matrix is an 11 Method as mentioned before, the first step in by 11 matrix. The elements of the matrix water quality zoning is the selection of should be stabilized using the Max-Min opera- clustering criteria, which are the concentrations tor. Considering the concentrations of nine se- of water quality variables. In this study, for lected water quality variables, similar reaches classification based on river water quality, nine based on the different values of λ cuts are de- water quality variables of termined and ranked from the less polluted to the most polluted zone. In the following sec- TDS,COD,BOD,SO2−,NO ,TSS,Cl−, DOandPO3− 4 3 4 tions, considering different water uses, the re- were considered. These variables have been sults of classification for each of the λ cuts are partially deviated from the standards. Table 2 explained: presents the selected water quality variables and River water quality zoning considering river the standard values for different water uses. Ta- water quality and aquatic life Water quality ble 3 presents the location of different reaches variables used in this section and their corre- in the study area and their water quality sam- sponding standard described in the previous pling points. The reaches downstream of Dark- sections. The results of fuzzy clustering analy- hovain, were not considered in this study be- sis are presented in Table 5. According to the cause the quantitative and qualitative charac- table, for a small value of λ (less than 2.9), all teristics of the river were considerably affected of the reaches are located in a same class by by the backwater of the Persian Gulf and the increasing the λ value to 3.5; the reaches are order of magnitude of concentration of water classified into two classes. By increasing, the λ quality variables in this zone is considerably value to the values more than 6.5, the nine different compared with the concentration in reaches that are located in downstream of Dez the other reaches. The results of the c-mean and Gotvand , is located in different clustering method considering the river water classes. As presented in Table 5, the reaches quality standard, are presented in Table 4. As it can be ranked from the less polluted to the most is obvious in this table, the river water quality polluted for each value ofλ . can be classified considering different number Selecting a λ value equal to 12.5 classifies the of classes. The results of Karoon-Dez River reaches to five different classes. Reach seven water quality zoning for 3 to 7 classes are pre- (in Gargar branch of Karoon River near to the sented in Table 4. This table shows the classifi- Gargar-Band e Ghir) that is located in a sepa- cation of the river water quality from less pol- rated class, has the most critical water quality luted reach (class 1) to the most polluted one condition. The concentration of some water (class 9). In all clustering, reach 7, which is lo- quality variables such as COD, BOD, TSS, CL¯ cated in downstream section of the Gargar and Hardness is usually more than the recom- River, is the most polluted reach considering mended standard in this reach. This critical

23 Iranian J Env Health Sci Eng, 2004, Vol.1, No. 2, pp.16-27

condition is mainly due to discharging agricul- River water quality zoning can also be con- tural return flow and wastewater of fish hatch- ducted considering irrigation water uses. The eries, in the Gargar River. selected water quality variables for evaluating The reaches between Band e Ghir and Om el Karoon and Dez River water quality for irriga- Tomair that have a similar water quality condi- tion purposes are SAR,TDS and Cl − . The re- tion are located in the same class. sults of fuzzy clustering analysis in such a case drain, effluent of Ramin and Zargan power are presented in Table 7. plants, wastewater of Ahvaz city and large in- dustries, which are located in these reaches, are Table 2: Selected indicator water quality variables for the main pollution sources of the polluted zone. different uses and their recommended standards As shown in Table 5, reach 1 located immedi- River water Irrigation ately in downstream of reach has the Drink use Selected quality use best water quality condition and is located in a standard variable standard standard (mg/lit) separated class. Although, reach 3 which is lo- (mg/lit) (mg/lit) cated immediately downstream of Gotvand dam has an acceptable water quality condition, the TDS 1200 1000 1200 water quality in reaches 1 and 3 is partially DO >4 - - different and these two reaches are located in SO4 250 250 - different classes due to discharging Masjed NO3 44 10 30 Soleiman domestic wastewater and agricultural − drains in the Gotvand region. Fig. 3 illustrates Cl 200 200 200 water quality zoning using fuzzy clustering BOD 10 3 - method for a λ value equal to 12.5. COD 40 20 - River water quality can also be classified con- PO4 0.7 - - sidering drinking water uses. Eight water qual- TSS 0 0 - ity variables are selected to evaluate the river Total - 500 - water quality for drinking uses. These water Hardness quality variables were TDS, COD, BOD, SO4, ¯ SAR - - * NO3, TSS, Cl and Hardness. The results of * The standard of SAR is different depending on the fuzzy clustering analysis for drinking water value of hydraulic conductivity. This variable hse not uses are presented in Table 6. been considered in analysis

Table 3: Karoon-Dez River reaches selected for quality zoning

Reach Reach Location Indicator Station Location Reach Name Number Number Between Pol e Between Dez Dam and 1 Dez-ChamGolak 7 Koshtargah and Band Gargar-Band e Ghir Dezful City e Ghir Between Dezful City Between Band e Ghir 2 Dez-Band e Ghir 8 Karoon-Band eGhir and Band e Ghir and Veis City Between Gotvand Dam Between Veis City and 3 Karoon-Gotvand Dam 9 Karoon-Zargan and Shooshtar City upstream of Ahvaz Between Shooshtar City 4 Karoon-Band e Mizan 10 Ahvar Region Karoon-Pol e Panjom and Band e Mizan Between Shooshtar City 5 Gargar-Pol e Koshtargah Between downstream and Pol e Koshtargah 11 of Ahvaz and Karoon-Om el Tommair Between Band e Mizan 6 Shotait-Band e Ghir Darkhovain and Band e Ghir

24 Mohammad Karamouz et al: River Water Quality…

Table 4: The results of C-Mean clustering analysis based on the water quality for aquatic life

The number of classes Class1 Class2 Class3 Class4 Class5 Class6 Class7 3 1-3-4-5 2-6-8-9-10-11 7 _ _ _ _ 4 1 3-4-5 2-6-8-9-10-11 7 _ _ _ 5 1 3-5 4 2-6-8-9-10-11 7 _ _ 6 1 3-5 4 2-6-8-9-10 11 7 _ 7 1 3-5 5 4 2-10 8-9-11 7

Table 5: The results of fuzzy clustering analysis based on the water quality for aquatic life

The number Class1 Class2 Class3 Class4 Class5 Class6 Class7 Class8 Class9 λ of classes

1-2-3-4-5-6- 2.9 1 ______7-8-9-10-11 1-2-3-4-5-6- 3.5 2 7 ______8-9-10-11

2-3-4-5-6-8-9- 6.5 3 1 7 ______10-11

2-6-8-9- 10.5 4 1 3-4-5 7 _ _ _ _ _ 10-11

2-6-8-9- 12.5 5 1 3 4-5 7 _ _ _ _ 10-11 6-8-9- 20.5 6 1 3 4-5 2-10 7 _ _ _ 11 30.8 7 1 3 4-5 8-6 2-10 9-11 7 _ _ 35.9 8 1 3 5 4 8-6 10-2 9-11 7 _ 72.8 9 1 3 5 4 8-6 10-2 9 11 7

Table 6: The results of fuzzy clustering analysis based on the water quality for drinking uses

The number Class1 Class2 Class3 Class4 Class5 Class6 Class7 Class8 Class9 λ of classes

1.6 1 1-2-3-4-5-6-7-8-9-10-11 ______

_ 1.7 2 1-2-3-4-5-6-8-9-10-11 2 7 _ _ _ _ _

2-4-5-6-8- 3.2 3 1-3 7 ______9-10-11 2-6-8-9- 3.5 4 1-3 4-5 7 _ _ _ _ _ 10-11

2-8-9- 4.6 5 1-3 4-5 10-6 7 _ _ _ _ 11 6.8 6 1-3 4-5 10-6 8-9-11 2 7 _ _ _ 7.4 7 1-3 4-5 10-6 9-8 11 2 7 _ _ 9.3 8 1 3 4-5 10-6 9-8 11 2 7 _ 12.3 9 1 3 4-5 6 10 9-8 11 2 7

25 Iranian J Env Health Sci Eng, 2004, Vol.1, No. 2, pp.16-27

Table 7: The results of fuzzy clustering analysis based on the water quality for irrigation uses

The number of Class1 Class2 Class3 Class4 Class5 Class6 Class7 Class8 λ classes

1-2-3-4-5-6-7-8- 13 1 ______9-10-11

2-6-7-8-9- 13.3 2 1-3-4-5 ______10-11

2-6-7-8-9- 17.8 3 1-3 4-5 _ _ _ _ _ 10-11

2-6-8-9-10- 19.2 4 1-3 4-5 7 _ _ _ _ 11 22.8 5 1-3 4-5 2-6-8-9-10 11 7 _ _ _ 2-6-8-9- 29.7 6 1-3 4 5 11 7 _ _ 10 2-6-8-9- 49.7 7 1 3 4 5 11 7 _ 10 65.3 8 1 3 4 5 2-8 6-9-10 11 7

DISCUSSION signed to one cluster because it may be located between the clusters but the c-mean as an ad- By comparing water quality data of the sam- vanced crisp clustering, does not have such de- pling points located in a class with the water ficiency and can provide almost similar clus- quality standards, the developed clusters can be ters. The main problem of the c-mean and the ranked. The results show that reach 7 (Gargar - fuzzy methods is that their results are depend- Ban e Ghir) and the reach located between ent on the selected distance criteria and simi- Darkhovain and Persian Gulf, which is affected larity matrix, respectively. by the backwater of the Persian Gulf, have a The results of c-mean method are almost simi- critical water quality condition. Comparing the lar to the results of the fuzzy method. Fuzzy results of c-mean crisp and fuzzy clustering analysis is more efficient and natural than other methods (Tables 4 and 5) show that the clusters methods of clustering or WQI methods in water are somehow different. For example, when the quality zoning. In water quality assessment, es- river reaches are classified to three classes, pecially when WQI score is close to the thresh- fuzzy clustering method separates the reaches old between normal and abnormal condition, c- with the best (reach 1) and the worst (reach 7) mean or fuzzy clustering methods can be ap- water quality condition from the other reaches. plied as alternative tools. However, in c-mean method, reaches 1, 3, 4 The results show that the proposed models can and 5 with partially different water quality are be affectively used for water quality zoning in located in the same class. When the river river systems and in ranking the reaches from reaches are classified to four classes, two mod- the most critical to the least critical water qual- els provide the same clusters (Tables 4 and 5). ity condition considering different water quality The fuzzy clustering method can provide better variables. clusters comparing to the crisp relation method, when a river reach can not adequately be as-

26 Mohammad Karamouz et al: River Water Quality…

ACKNOWLEDGEMENTS clustering analysis. Water Resources Bulle- tin, 28: 525-33. The valuable contribution of the managers and Ott W R (1980). Environmental Indices-Theory engineers of Khuzestan Environmental Protec- and Practice. Ann Arbor Science Publishers, tion Agency for providing data and site maps as Inc., Ann Arbor, Michigan. well as the technical assistance are hereby ac- Ross J T (1997). Fuzzy Logic, with Engineering knowledged. Applications. McGraw-Hill Inc. Wang P (1983). Theory of Fuzzy Sets and Its Application. Shanghai Science and Technol- REFERENCES ogy Publishers, Shanghai, China. Zadeh L A (1965). Fuzzy sets. Information and Horton R K (1965). An index-number system control, 8: 338-53. for rating water quality. Water Pollution Zadeh L A (1973). Outline of a new approach Control Fed, 37: 300-306. to the analysis of a new systems and design Kung H T, Ying L G, Liu Y C (1992). A com- process. IEEE Trans, SMC, 3: 28-44. plementary tool to water quality index: fuzzy

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