Classification of drinking water quality using Schoeller ...

Natural Resource Management, GIS & Remote

Sensing

Volume 1, Number 1, 61-71

March 2019

DOI: 10.22121/ngis.2019.85268

© 2019 The Authors.

Published by Firouzabad Institute of Higher Education, Firouzabad, Fars, Iran

Classification of drinking water quality using

Schoeller diagram in GIS

Saeed Negahban1

, Marzeyeh Mokarram2, Gholamreza zare3

1Assistant Prof., Department of Geography, Shiraz university

2Assistant Prof., Faculty of Natural Resource, Shiraz university

3phd in geomorphology

[email protected]

(Received: January 25, 2019 / Accepted: February 28, 2019)

Abstract Drinking water with high quality is one of our most vital resources, and when

our water is polluted it is not only devastating to the environment, but also to human

health. So the aim of the research is investigation groundwater quality using Schoeller

diagram method in north of Fars province, southeast Iran. For determination of

groundwater quality, parameters of chlorine (Cl), sodium (Na), sulfate (So4), total

dissolved solids (TDS) and total hardness (TH) were used. Using Inverse Distance

Weighting (IDW) interpolation maps of each parameters using inverse distance

weighting (IDW) method and then fuzzy and AHP method were determined. The results

of fuzzy method showed that, except the parts of south, all of the area was not suitable

for So4, Cl, and Na that had the value close to 0. Also except the some parts of north was

suitable for TDS and TH that had the value close to 1.

Keywords: Groundwater quality; Inverse Distance Weighting (IDW); Schoeller; Fuzzy-

AHP method

1. Introduction

The availability of high-quality water is a key determinant for human, animal and plant

survival. Without water, living things could not survive, making water quality one of the

most important factors in whether anything can inhabit an area. Understanding relations

can improve management and utilization of the groundwater resource by clarifying

relations among groundwater quality, aquifer lithology, and recharge type (Ostovari et

al. 2013). Recently by using geography information system (GIS) and sample data of

well, prepare interpolation map. The GIS is an effective tool the estimation of the spatial

distribution of environmental variables (Ordu and Demir, 2009; Rabah et al. 2011;

Ritchie and Charles, 1996; Dekker et al., 2002 and Schalles et al., 1998). The use of GIS

https://doi.org/10.7232/aotp.2017.16.1.001

62 NRM, GIS & RS, 2019, Vol. 1, No. 1



and remote sensing in water quality monitoring have improved observation and

detection, which have increased public and scientific awareness of water quality related

issues and threats (Heisler et al. 2008). GIS can also be used to assess relationships

between water quality and land use and land cover (Sharma et al. 2016; Zheng et al.

2016a). Zheng et. al assessed ground water contamination vulnerability from agricultural

non-point source contamination using a GIS-base groundwater contamination risk

assessment model (2016).

Spatial prediction and surface modeling of water properties has become a common topic

in water science research. Interpolation can be undertaken utilizing simple mathematical

models (e.g., inverse distance weighting, trend surface analysis and splines), or more

complex models (e.g., geostatistical methods, such as kriging) (Negreiros et al. 2011).

Ordianary Kriging (OK), Universal Kriging (UK), Inverse Distance Weighting (IDW)

and Radial Basis Function (splines) are four ways to interpolate water quality. Some

researchers found that kriging methods out performed IDW or Radial Basis Function

(Splines) (Kravchenko and Bullock 1999; Panagopoulos et al. 2006). IDW

(Panagopoulos et al. 2006) and Splines (Kravchenko 2003; Keshavarzi and Sarmadian

2010) are also commonly used classical interpolation methods to analyze the spatial

variability of water quality.

The fuzzy set theory concept was introduced by Zadeh (1965). To some extent, fuzzy

method is a mathematical language that translate vague statements into a mathematical

formalism (McNeil and Thro 1994). Sadiq et al. (2004) used fuzzy method to risk

management for drilling waste discharges. Fuzzy method was also employed to

evaluated urban air quality in Istanbul (Guleda et al. 2004). Wang used fuzzy method to

assess the quality of air, water and soil in Zhuzhou City (Wang 2002).

The aim of this research is to employ the geostatistic analysis (IDW) and fuzzy AHP for

prediction of water quality in North of Fars province, southeast Iran.

2. Material and method

2.1. Case study

This study area was located in north of Fars province, southeast of Iran. It has an area of

about 6657.25 km2, and is located at longitude of N 29° 18΄- 30° 25΄and latitude of E 52°

04΄ to 53° 26΄ (Figure 1). The altitude of the study area ranges from the lowest of 1,530

m to the highest of 3,099 m.

Classification of drinking water quality … 63



Figure1. Position of the study area

For prediction the variability of groundwater quality, chlorine (Cl), sodium (Na), sulfate

(So4), total dissolved solids (TDS) and total hardness (TH) were prepared (Table 1). For

making the spatial distribution map of each parameters was used 122 wells that local

position of the wells show in Figure 2.

Table 1. Statistic characteristics of chemical compositions (mg/l) in the groundwater’s

of the study area

Minimum Maximum Average STDV

Na 0.12 122.77 14.06016 21.96956

So4 0.15 56.35 6.923279 8.715891

Cl 0.25 173 21.76066 37.98701

TDS 315 11540 2040.91 2581.938

TH 200 6750 965.1 1250.4

64 NRM, GIS & RS, 2019, Vol. 1, No. 1



Figure 2. Local position of the wells in the study area

2.2. Method

Preparing interpolation map

In the study for preparing interpolation maps for each parameters was used inverse

Distance Weighted (IDW). To predict a value for any unmeasured location, IDW will

use the measured values surrounding the prediction location. Assumes value of an

attribute z at any unsampled point is a distance-weighted average of sampled points lying

within a defined neighborhood around that unsampled point. Essentially it is a weighted

moving average (Burrough et al. 1998):

10

1

( )ˆ( )

nr

i ij

i

nr

ij

i

f x d

f x

f

(1)

Where x0 is the estimation point and xi are the data points within a chosen neighborhood.

The weights (r) are related to distance by dij.

Fuzzy and AHP method

A fuzzy membership function is described by a membership function μA (T) of A. Each

element t σ ∈ T a number as μ A (T σ) in the closed unit interval [0, 1] associates for

membership function. The number μ A (T σ) shows the degree of membership of t σ in

A. The attention used for membership function μ A (T) of a fuzzy set A is Α: T→ [0, 1]

The following function was used for all parameters of the water.




0

( ) /

1

t a

T f t t a b a a t b

t b

(2)

Where t is the input data and a, b are the limit values.

In order to define the fuzzy rules was used Schoeller diagram (Table 2).

Table 2. Water quality classification using Schoeller diagram (Schoeller, 1965)

So4 Cl Na TH TDS The degree of

water quality for

drinking

<5 <5 <10 <190 <280 High

5-10 5-10 10-15 191-250 281-500 Medium

11-15 11-20 16-20 251-600 501-1000 Low

16-25 21-30 21-30 601-1550 1001-3500 Very low

>26 >31 >31 >1551 >3501 Non-Drinking

Using overly five factors for preparing water quality was used AHP method. These

values are given by a scale from 1 to 9, where 1 means that the two elements being

compared have the same importance and 9 indicates that of the two elements one is

extremely more important than the other.

3. Results

In order to interpolation maps for each parameter was used IDW method that show in

Figure 3. The lowest and the maximum output in IDW is 1.5 and 79.93 mg/l for Ca. 0.28

and 172.25 mg/l are lowest and maximum value for Cl. The minimum value for Na and

So4 are 0.12 and 0.21 mg/l respectively. While the maximum value for Na and So4 are

120.94 and 56.31 mg/l respectively. So the north of the study area is better quality than

the south of the study area. Based on permissible limits for each parameters were defined

membership function and then were created fuzzy maps for chlorine (Cl), sodium (Na),

sulfate (So4), total dissolved solids (TDS) and total hardness (TH) in ArcGIS software

(Figure 4).

66 NRM, GIS & RS, 2019, Vol. 1, No. 1



(a) (b)

(c) (d)

(e)

Figure 3. Interpolated maps of the groundwater quality parameters generated using by

IDW. (a): So4; (b): Cl; (c): TDS; (d): Na; (e): TH

According to whatever of the So4, Cl, TDS, Na and TH value in the water have fewer,

water quality is higher, lower value had given 1, high value had given 0 and other value

had given between 0 to 1. Results of fuzzy method showed that, except the parts of south,




all of the area was not suitable for So4, Cl, and Na that had the value close to 0 (Figure

4). Also except the some parts of north was suitable for TDS and TH that had the value

close to 1 (Figure 4).

(a) (b)

(c) (d)

(e)

Figure 4. Fuzzy maps for each parameter for determining the water quality in the study

area. (a): So4; (b): Cl; (c): TDS; (d): Na; (e): TH

68 NRM, GIS & RS, 2019, Vol. 1, No. 1



The AHP method was applied on the fuzzy parameter maps and the pairwise comparison

matrix which was used for preparation of the weights for each parameter was given in

Table 3. Results showed that the most important factor in water quality was Na with

weights of 0.39. While the least important water parameters was So4 with weights of

0.090 (Table 3).

Table 3. Pairwise comparison matrix for water quality.

Parameters Na TDS Cl TH So4 Weight

Na 1 2 3 4 5 0.390

TDS 1/2 1 2 3 4 0.250

Cl 1/3 1/2 1 2 3 0.160

TH 1/4 1/3 1/2 1 2 0.112

So4 1/5 1/4 1/3 1/2 1 0.090

Finally based on the fuzzy maps for each parameters (Figure 5) and weight of each

parameter that was calculated using AHP method (Table 1), the final fuzzy map was

determined (Figure 5). The value of final fuzzy map was between 0 to 0.98 where showed

the some parts of the study area had high quality (for value more than 0.75), medium

quality (for value between 0.5 to 0.75), low quality (for value between 0.25 to 0.5) and

very low quality (for value between 0 to 0.25) the results show that only some parts of

south had good water quality with value close to 1 (Figure 5). Then, the fuzzy map

reclassified in four classes consisted of very low (2624.64 km2), low (533.41 km2),

medium (1478.64 km2) and high (2020.55 km2) (Figure 5 and Table 2).




Figure 5. Fuzzy-AHP combination map for water quality.

After reclassifying the fuzzy map prepared in the four classes that show in Figure 6 and

Table 4.

Figure 6. Map of the fuzzy classification.

70 NRM, GIS & RS, 2019, Vol. 1, No. 1



Table 4. The area (%) for each of the classes for water quality

Class Area (km2)

Very low 2624.642

Low 533.4191

Medium 1478.64

High 2020.55

4. Conclusion

This aim of the study was determination of the groundwater quality in north of the Fars

province, southeast of Iran. The results of spatial distribution each of parameters by IDW

show that the maximum value of parameters is located in south of the study area. The

results of fuzzy method showed that, except the parts of south, all of the area was not

suitable for So4, Cl, and Na that had the value close to 0. Also except the some parts of

north was suitable for TDS and TH that had the value close to 1.

The major difference between this study and other studies is that we combine two

methods (fuzzy and AHP) for determination of water quality. Similar research was done

by Shobha et al. (2013). Shobha et al. (2013) used fuzzy-AHP for determination of water

quality. In the study, a membership function for the fuzzy rule based system for each salt

was developed and the weights for each parameter was calculated using Analytic

Hierarchy Process (AHP) that relies on pair wise comparison. The system showed that

out of 24 districts of Karnataka state, ground water from 51.78% bore-wells was not

feasible for consumption.

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