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River Water Quality Zoning: a Case Study of Karoon and Dez River System

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River Water Quality Zoning: a Case Study of Karoon and Dez River System 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 Dez River System *M Karamouz 1, N Mahjouri 2, R Kerachian 3 1School of Civil and Environmental Engineering, Amirkabir University, Iran 2 Faculty of Environmental Engineering, University of Tehran, 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 = i j (2) data obtained from observation such as water i I j quality data provided by a monitoring system. 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 ) = !1 and µR (x j, xk ) = !2 " µR (xi , xk ) = to describe its location in m dimensional space, ! where ! min[! ,! ] 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 ! 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 # rjk ) (15) lows: a) Selection the clustering criteria Where V is the maximum operator and F 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.
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