journal of applied hydrology (2) (2) (2015) 45 - 61 ...€¦ · m. daneshfar and h. zeinivand /...

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Journal of Applied Hydrology (2) (2) (2015) 45 - 61

Journal of Applied Hydrology

Application of Frequency Ratio, Weights of Evidence and Multi

Influencing Factors models for groundwater potential mapping

using GIS

* E-mail of Corr. author: [email protected]; [email protected]

Article history: Received: 5 Jan. 2015 Revised: 12 March 2015 Accepted: 08 Apr... 2015

1. Introduction

Water is an initial and necessary need of

humankind. Groundwater is a main resource

of water in many regions, and consists of;

consistent temperature, widespread

availability, low pollution, excellent natural

quality and very low development cost; thus

use of groundwater resources has increased in

many regions of the world especially in arid

and semi-arid regions. However, increases of

population, agricultural, industrial and

domestic stir can cause groundwater resources

reduction. Climate change and watershed bad

planning can lead to decrease in surface water

in many regions of the world; particular arid

Mania Daneshfar1, Hossein Zeinivand2*

1 MSc Student, Dept. of Range and Watershed Management Engineering, Lorestan University, Khorramabad,

Lorestan, Iran.

2 Assistant Prof., Dept. of Range and Watershed Management Engineering, Lorestan University, Khorramabad,

Lorestan, Iran.

Abstract

Groundwater resource is a very important water resource that has stable temperature, clear, tidy and confident. In recent

years, population growth, industrialize and need to food and water, have exposed the groundwater resource on the risk.

Reduction of water resources is a main problem in throughout the world. In this research groundwater potential

mapping was obtained in Norabad plain, Lorestan, Iran. Twelve parameters were selected, and used as the input data.

The parameters influencing in potential groundwater mapping in the study area were altitude, slope aspect, slope angle,

plan curvature, Topographic Wetness Index (TWI), land use, soil, lithology, drainage density, distance from river, fault

density and fault distance. Then, 106 wells with productivity higher than 10 (m3/s) were mapped in ArcGIS10.2.

Subsequently, 70% of groundwater dataset (74 wells) were selected randomly as training and 30% remaining (32 wells)

as testing data set. Then groundwater potential maps were created using Frequency Ratio (FR), Weights of Evidence

(WoE) and Multi Influencing Factors (MIF) models in ArcGIS10.2. The final map of groundwater potential for FR,

WoE and MIF model was classified into five classes (very low, low, moderate, high and very high) using quantile

method. Finally, ROC (Receiver Operating Characteristic) curves obtained for validation of FR, WoE and MIF models

using SPSS19. Subsequently, Area under curves (AUC) was calculated. The AUC values of FR, WoE and MIF models

were almost the same and set in the excellent class. Hence, the provided mapping of groundwater potential in this area

is a reliable tool for planning in future.

Keywords: Groundwater potential, FR, WOE, MIF, Validation, ROC, AUC

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http://jap.haraz.ac.ir/article-1-57-en.html

M. Daneshfar and H. Zeinivand / Journal of Applied Hydrology. 2 (2) (2015) 45-61 46

and semi-arid regions, and can also cause

down falling groundwater level. Since,

nomination of potential groundwater is very

important, for suitable groundwater

management and planning of future, many

researchers have mapped potential of

groundwater in various region) Davoodi

Moghaddam et al. 2013).

Based on the researchers' findings,

upstanding gauge have been unfit specific,

consist of; time consuming, uneconomical and

unsuccessful in some part time; thus, they

have used modern techniques for mapping the

potential of groundwater. Geographic

Information System (GIS) applies a suitable

role in potential of groundwater. GIS based

models use different layers for mapping

groundwater productivity (Rahmati et al;

2014). For obtaining groundwater potential

maps, different GIS based models i.e. FR,

WoE, SVM, DT, MIF and etc. have been

applied.

Several researches have applied GIS based

models for potential of groundwater mapping:

Rahmati et al (2014); Mansouri and Mezouari

(2015); Panda and Shahkar (2013); Davoudi

Moghadam et al. (2013); Arkoprovo et al.

(2012); Manap et al. (2012); Machiwal et al.

(2011); Pardahan (2009); Cools et al. (2006);

Dawoudet al. (2005). Razandi et al. (2015)

used FR, CF and AHP models for

groundwater potential mapping in Varamin

plain. Manap et al. (2012) applied FR model

for potential mapping in Negeri Sembilan.

Corsini et al. (2009); Oh and Lee (2010); Lee

and et al. (2012) and Ozdemir (2011) used

WoE model for potential mapping. Selvam et

al. (2015); Mwega et al. (2013); Arkoprovo et

al. (2012); Jastori et al. (2012); Bagiaraj et al.

(2012) and Maghesh et al. (2012a,b) applied

MIF techniques for groundwater potential

mapping.

In this study, the main objective was to

provide potential mapping of groundwater in

Norabad plain, Lorestan province, Iran. The

methods used were FR, WoE and MIF.

Potential mapping helps the decision makers

and politician to make suitable decision and

better groundwater management. It isa helpful

tool for suitable land use particularly

agricultural and industrial type for the study

area.

2. Materials and Methods

2.1. Study area

The Norabad plain in Lorestan province in

west of Iran, is located between 47◦ 27՜ E to

48◦ 18՜ E longitudes and 34◦ 50՜ N to 34◦ 18՜ N latitudes (Fig. 1). The total area of Norabad

plain is 3519.37 km2. The number of

residential population is 140000. This study

area has 500 villages. Gamasyab and

Seymareh rivers are in the west, Kashkan

River is at southeast and Badavar River is at

the middle of Norabad plain. The weather is

cold mountain with snowy winter and very

cold, and moderate summer. Annual rainfall is

529 mm, the average daily minimum

temperature is -1◦C and the average maximum

temperature is 20◦C. Absolute maximum is

39◦C and absolute minimum temperature is

27.5◦C. Norabad plain has 104 freezing days

in year. Maximum, minimum and average

altitudes are 3243, 932 and 1877 m a.s.l

respectively. The main crops are cereals.

Resource of water includes surface water, 123

deep and semi-deep wells, 20 diversion dams,

40 storage water pool units and 4 pumping

stations.

2.2. Methodology

The groundwater potential mapping contains

three main process, 1) spatial database

construction. 2) Analysis of relationship

between wells locations, and 3) validation of

research outcome (Razandi et al. 2015). In this

research, the potential of groundwater map

was created using 3 methods of FR, WoE and

MIF at Norabad plain, Lorestan province, Iran.

At the first step, 74(70%) of groundwater data

were randomly selected for training of the

models and the remaining 32 (30%) were

applied for the models validation. Figure 1

shows the groundwater wells locations

(training and validation data set) and the

methodology used in this study area is

presented in Fig. 2. Twelve groundwater –

related factors were used in calculating the

probability. These factors are altitude, slope

aspect, slope angle, plan curvature, TWI, land

use, soil, lithology, drainage density, distance

from river, fault density and fault distance

(Fig. 3). These layers were in raster format.

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47 M. Daneshfar and H. Zeinivand / Journal of Applied Hydrology. 2 (2) (2015) 45-61

The cell size of these layers was 30*30 m.

Some layers for example: slope angle, slope

curvature, river density, river distance, and

TWI map obtained from DEM. The TWI

revealed the impact of topography on the

position and scale of local sources of runoff

generation.

Fig. 1 Groundwater wells locations map of Norabad Plain, Lorestan province, Iran.

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Fig. 2 Flowchart showing the methodology in the study area

2.2.1 Inputdata 2.2.1.1 Altitude

The altitude map was produced using DEM

30*30 m cell size. In the present study area,

the altitude map classified to five classes with

quantile method in the ArcGIS10.2 software,

and was grouped into five classes: 932-1524,

1524-1742, 1742-1859, 1859-2000 and 2000-

3243 m a.s.l (Fig. 3a).

2.2.1.2 Slope aspect

The slope aspect map was produced using

DEM and it is defined as the direction of the

maximum slope of the train surface. The slope

aspect is related to the physiographic trends

and the main precipitation direction

(Ercahoglu and Gokceoglu, 2002). In this

study, the slope aspect map was grouped to

nine classes (Fig. 3b).

2.2.1.3 Slope angle

The slope angle map obtained from DEM.

This map can be presented as a surface

parameter that has a main role in filtration and

velocity of runoff. The moisture content and

pore pressure could be influenced at local

scales, whereas the regional hydraulic

behavior is controlled by slope angle patterns

at larger scales (Mancini et al. 2010). In this

paper, slope angle map was classified to seven

classes with quantile method: 0-4, 4-6, 6-9, 9-

12, 12-16, 16-22 and 22-76 in degree (Fig. 3c).

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2.2.1.4 Plan curvature

Curvature analysis allows dividing area to

concave, flat and convex surface. Plan

curvature is the reciprocal of the radius of a

circle that is tangent to a point on a curve

(Roberts, 2001). In this study, the curvature

map obtained from DEM and was classified to

three classes with quantile method in the

ArcGIS10.2 software: concave, flat and

convex (Fig. 3d).

2.2.1.5 TWI

Topographic Wetness Index (TWI) is another

topographic parameter that influences in

potential of groundwater. TWI was calculated

by Eq. 1:

)tan

(

aLnTWI (1)

Where, a, is the specific cumulative unslope

area draining through a point (per unit contour

length), and tanβ is the slope angle of specific

grid which is applied to replace approximately

the local hydraulic gradient under steady state

conditions.

The TWI defined as the soil moisture and

surface saturation that can quantify the control

of local topography on hydrological process

(Regmi et al. 2013). In this research, TWI was

classified to three classes using quantile

method: 3-6, 6-8 and 8-18 (Fig. 3e).

2.2.1.6 Land use

Land use map is helpful for identifying the

land use classes such as forest, agricultural

land, garden, water and other infrastructures

on the earth such as roads, manufacturing

plants and harbors (Devokota et al. 2013). In

this area, there were six classes for land use:

farming, rangeland, garden, forest, urban and

woodland (Fig. 3f).

2.2.1.7 Soil

Soil is very important factor in potential

groundwater mapping. Soil ranking is related

to infiltration capacity. Infiltration rate

depends on soil thickness and grain size. There

are three classes of soil in this study area:

Entisols, Inceptisols and vertisols (Fig. 3g).

2.2.1.8 Lithology

The lithology influences on the porosity and

permeability of aquifer (Azizy et al. 2010;

Chowdhury et al. 2010). Groundwater

potential is high at the alluvial sediment

mainly. The lithology of study area consists of

many Geo-units that have Cenozoic, Mesozoic

or Cenozoic-Mesozoic age (Fig. 3h).Finally,

the Lithology map was classified into several

Geo units as shown in Table 5.

2.2.1.9 Drainage density

Drainage pattern shows the specification of

surface or underlying lithology. Drainage

density map was prepared from drainage

length per square kilometer (Manap et al.

2012). Drainage density was calculated using

Eq. 2:

A

DD

n

i id

1 (2)

Where ΣDi is the total length of all streams in

the mesh (km) and A is the area of the grid

(km2) (Rahmati et al. 2014).

In this study, the drainage density was

classified to five classes with quantile method:

0, 0-0.11, 0.11-0.21, 0.21-0.31, and 0.31-0.68

km-1 (Fig. 3i).

2.2.1.10 Distance from river

Rivers have an undeniable role in groundwater

mapping. Distance from river obtained from

DEM and then classified to five classes using

quantile method: 0-806, 806-1742, 1742-2765,

2765-4057 and 4057-9528 m (Fig. 3j).

2.2.1.11 Fault density

The potential of groundwater has a strong

correlation with the tectonic feature as faults.

Groundwater potential is very well in the

faults and decreased sharply with distance

from it. This map classified to four classes

with using quantile method: 0, 0-0.16, 0.16-

0.33 and 0.33-1.12 km-1 (Fig. 3k).

2.2.1.12 Fault distance

High groundwater recharge occurs in pore and

fracture of rock and stone on the earth mainly.

Without doubt, fault causes recharge of

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groundwater better than without any fault or

fracture of rock. In this research, fault distance

map classified to five classes: 0-710, 710-2051,

2051-4971, 4971-9390 and 93090-20122 m

(Fig. 3l).

2.2.2 Frequency Ratio (FR) model

A FR model is a positive instrument to obtain

eventuality (probabilistic) correlation among

dependent and independent variables including

multi – classified layers. Usage of the FR

method is downright and the results are

straightforward to realize and follow. The FR

is the ratio of the area where locate the wells to

the total study area.To calculate the FR value

for each class or factor, the ratio of well

occurrence to nonoccurrence is obtained

(Table1)(Ozdemir and Altural, 2013). The FR

values are calculated using Eq. 3, (Neshat et al.

2013).

n

i pix

jpix

m

i xi

xipix

N

xN

S

SN

FR

1

1

)(

)(

(3)

Npix(sxi), is number of wells pixels in each

class of factor x. Npix (xj) is number of pixel

within factor xj, m is number of classes in the

factor xj, and n is the number of factors in the

study area. (Jaafari et al,2014).

2.2.3 Weights of Evidence model (WoE)

This research establishes the application of

total WoE method for potential of groundwater

mapping using GIS in Norabad plain. The

WoE was used to gauge each relevant factor`s

weight. These weights can be extracted using

analysis of particular correlation among

groundwater wells position and each of the

fitting factors. For using the WoE method,

positive weights (w+) and negative weights (w-

) should be calculated. Conditional probability

is calculated using Eq. 4.

)(

))](*))(([)(

i

ii

Bp

sPSBpBSP (4)

Where P(Bi|S) is the conditional probability to

have Bi given S, p (s) is the prior probability to

discover S inside the study area (AS) , P(Bi) is

prior probability to discover the class Bi inside

the study area (AS).The conditional

probability to have S when the class Bi given

is (Eq. 5):

)^(

))](*))^(([)(

i

ii

Bp

sPSBpBSP (5)

Where p (B^I |S) is the conditional probability

not to have the class Bi given s, P(s) is the

prior probability not to find the class Bi inside

the study area AS. Functions 6, 7, 8, 9, 10, 11

and 12 follow as:

AreaAS

AreaSSP )( (6)

AreaAS

AreaBBP i

i )( (7)

AreaAS

AreaBBP i

i^

)( (8)

)p(B

AreaB/BS Area )(

i

iiiBSP (9)

p(S)

AreaB/BS Area )( iiSBP i (10)

)p(B^

AreaB^/B^S Area )^(

i

iiiBSP (11)

p(S)

^AreaB/^BS Area )^( iiSBP i (12)

In WoE method, for each factor’s class

positive weight (Eq. 13), negative weight (Eq.

14), and C values were calculated.C is

difference between positive and negative

weights (Eq. 15).

)(

)(ln

i

i

BP

SBpW (13)

)^(

)(ln

i

i

BP

SBpW (14)

WWC (15)

If C is positive, the factor is favorable for the

groundwater productivity, and C is negative if

the factor is unfavorable.

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Then PP(s) or the value of posterior probability

is given by Eq. 16 (Corsini et al. 2009):

(16)

The values of WoE model in Noorabad plain

are given in Table 2.

2.2.4 Multi Influencing Factors (MIF)

model

MIF method is a GIS based model. The

influencing factors for groundwater

productivity mapping in this study are shown

in Table 3. The based on the effects of each

major and minor parameter a weightage of 1

and 0.5 are assigned respectively. The

cumulative weight of both major and minor

are considered for calculating the relative rates.

Again, the rate is further used to calculate the

score of each influencing factor. The score for

each influencing factor is calculated by Eq. 17

(Chowdhury et al. 2009; Chowdhury et

al.2010; Kaliraj et al. 2014 and Mancini et

al.2010; Arkoprovo et al. 2013).

(17)

Where A is major interrelationship between

two factors and B is minor interrelationship

between two factors.

This procedure is done for all input layers, and

spatial features relevant to the groundwater

were extracted (Table 4). Finally, all involved

thematic layers are combined to extract

potential groundwater map.

2.2.5 Validation

The Relative Operating Characteristic (ROC)

analysis was applied to nominate the severity

of groundwater potential map provided in this

study using FR, WoE and MIF techniques

(Akgun et al. 2012; Mohamady et al. 2012;

Pourghasemi et al. 2012; Ozdemir and Altural

2013). ROC curve analysis has been a current

method to determine the accuracy of a

diagnostic test (Pourghasemi et al. 2013a,b).

AUC is the Area under Curve of ROC curve,

which depicts the accuracy of potential

mapping (Yesilnacar, 2005). AUC shows a

perfect classification for an AUC=1 and

classification by change for an AUC=0.5.

3. Results and discussion

The influence layers of groundwater potential

were prepared in ArcGIS10.2 software. These

layers were altitude, slope aspect, slope angle,

plan curvature, Topographic Wetness Index

(TWI), land use, soil, lithology, drainage

density, distance from river, fault density and

fault distance. Then they were classified to

different classes using quantile method.

Afterwards, groundwater potential mapswere

obtained based on the FR, WoE and MIF

methods, and were classified to five classes

including very low, low, moderate, high and

very high (Fig 4).

In this study, the AUC were calculated for FR,

WoE and MIF equal to 0.972, 0.976 and 0.974

respectively. This result indicates that the three

models have almost the same efficiency for

mapping groundwater potential in Noorabad

plain (Fig 5).

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Fig. 3 Groundwater conditioning factors map of Norabad Plain, Iran; (a) altitude, (b) slope aspect, (c) slope angle, (d) plan curvature, (e)

TWI, (f) land use, (g)soil, (h) lithology, (i) drainage density (j) distance from river (k) fault density (l) fault distance

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53

M. Daneshfar and H. Zeinivand / Journal of Applied Hydrology. 2 (2) (2015) 45-61

Fig. 4 Groundwater potential maps based on (a) FR; (b) WoE, and (c) MIF models of NorabadPlain, Iran

0 20 40 60 80 100

0

20

40

60

80

100

100-Specificity

Sen

sitivity

0 20 40 60 80 100

0

20

40

60

80

100

100-Specificity

Sen

sitivity

AUC_WoE = 0.976 AUC_MIF = 0.974

B

0 20 40 60 80 100

0

20

40

60

80

100

100-Specificity

Sen

sitivity

AUC_FR = 0.972

Fig. 5 Area undercurve (AUC) calculated for FR, WoE and MIF models for mapping groundwater potential in Noorabad plain.

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Table 1. Spatial relationship between each conditioning factor and well locations using FR model in Norabad Plain, Iran

Parameters class No. of pixels

in domain

Percentage of

domain No. of well

Percentage of

springs

Frequency

ratio (FR)

932-1524 784889 20.07 13 17.56 0.87

1524-1722 785422 20.08 0 0 0

Altitude (m) 1722-1859 781248 19.97 35 47.29 2.36

1859-2000 782368 20.007 26 35.13 1.75

2000-3243 776480 19.85 0 0 0

Sum 3910407 100 74 100

Slope aspect Flat 5751 0.14 2 2.70 18.37

N 499871 12.78 7 9.45 0.73

NW 554182 14.17 7 9.45 0.66

E 393570 10.06 6 8.10 0.80

ES 388756 9.94 6 8.10 0.81

S 573703 14.67 8 10.81 0.73

SW 647733 16.56 20 27.02 1.63

W 469591 12.008 12 16.21 1.35

N 377250 9.64 6 8.10 0.84

Sum 3404785 87.06 65 87.83

Slope angle

(in degree) 0-4 449679 11.49 47 63.51 5.52

4-6 621210 15.88 20 27.02 1.70

6-9 713899 18.25 6 8.10 0.44

9-12 700980 17.92 1 1.35 0.07

12-22 609628 15.58 0 0 0

16-22 490449 12.54 0 0 0

22-76 324562 8.29 0 0 0

Sum 3910407 100 74 100

Plan

Curvature Concave 1739700 44.48 26 35.13 0.78

Flat 1326391 33.91 43 58.10 1.71

Convex 844316 21.59 5 6.75 0.31

Sum 3910407 100 74 100

TWI 3-6 1193983 30.53 2 2.70 0.08

6-8 1437546 36.76 26 35.13 0.95

8-18 1278878 32.70 46 62.16 1.9

Sum 3910407 100 74 100

Land use Farming 1509643 38.60 70 94.59 2.45

Rangeland 833535 21.31 1 1.35 0.06

Garden 14053 0.35 0 0 0

Forest 1532934 39.20 3 4.05 0.1

City 8070 0.20 0 0 0

Woodland 12172 0.31 0 0 0

Sum 3910407 100 74 100

Soil Entisols 650189 16.62 5 6.75 0.40

Inceptisols 3249736 83.10 69 93.24 1.12

Vertisols 10482 0.26 0 0 0

Sum 3910407 100 74 100

Lithology Cenozoic 2722644 69.62 70 94.59 1.35

Mesozoic 1119545 28.62 4 5.40 0.18

Cenozoic-

Mesozoic 68218 1.74 0 0 0

Sum 3910407 100 74 100

Drainage

density(km/k

m2)

0 1611590 41.21 4 5.40 0.13

0-0.11 586797 15.006 6 8.10 0.54

0.11-0.21 572194 14.63 7 9.45 0.64

0.21-0.31 579002 14.80 24 32.43 2.19

0.31-.68 560824 14.34 33 44.59 3.10

Sum 3910407 100 74 100

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55


Table 1: Continue

Parameters class No. of pixels

in domain

Percentage of

domain No. of well

Percentage of

springs

Frequency

ratio (FR)

Distance

from

river(m)

0-806 783745 20.04 42 56.75 2.83

806-1714 781685 19.98 21 28.37 1.41

1714-2765 782574 20.012 7 9.45 0.47

2765-4057 781365 19.98 3 4.054 0.20

4057-9525 781038 19.97 1 1.35 0.06

Sum 3910407 100 74 100

Fault

density(km/k

m2)

0 2099785 53.69 22 29.72 0.55

0-0.16 599336 15.32 28 37.83 2.46

0.16-0.33 605105 15.47 23 31.08 2.008

0.33-1.12 606181 15.50 1 1.35 0.08

Sum 3910407 100 74 100

Fault

distance(m) 0-710 725592 18.55 24 32.43 1.74

710-2051 846793 21.65 22 29.72 1.37

2051-4971 788600 20.16 20 27.02 1.34

4971-9390 780598 19.96 6 8.10 0.40

9390-20122 768824 19.66 2 2.70 0.137

Sum 3910407 100 74 100

Table 2 Spatial relationship between each conditioning factor and well locations using WoE model in

Norabad Plain, Iran

Parameter class

No. of

pixels in

domain

No. of

well W+ W- C

S2

(W+) S2 (W-) S(C) C/S(C)

932-1524 784889 13 -0.13 -0.11 -0.03 0.08 0.01 0.30 -0.09

1524-1722 785422 0 0 0.03 0 0 0.01 0 0

Altitude 1722-1859 781248 35 0.86 -0.39 1.25 0.03 0.02 0.21 6.00

1859-2000 782368 26 0.56 -0.27 0.83 0.04 0.01 0.23 3.64

2000-3243 776480 0 0 0.03 0 0 0.01 0 0

Sum 3910407 74

Slope

aspect Flat 5751 2 2.91 -0.02 2.93 0.50 0.01 0.71 4.10

N 499871 7 -0.30 -0.05 -0.25 0.14 0.01 0.39 -0.64

NW 554182 7 -0.40 -0.05 -0.36 0.14 0.01 0.39 -0.91

E 393570 6 -0.22 -0.04 -0.17 0.17 0.01 0.42 -0.41

ES 388756 6 -0.20 -0.05 -0.16 0.17 0.01 0.42 -0.38

S 573703 8 -0.31 -0.06 -0.25 0.13 0.01 0.37 -0.67

SW 647733 20 0.49 -0.19 0.68 0.05 0.01 0.25 2.74

W 469591 12 0.30 -0.11 0.41 0.08 0.01 0.31 1.32

N 377250 6 -0.17 -0.05 -0.13 0.17 0.01 0.42 -0.30

Sum 3910407 74

Slope angle (in degree)

0-4 449679 47 1.71 -0.61 2.32 0.02 0.02 0.20 11.61

4-6 621210 20 0.53 -0.19 0.73 0.05 0.01 0.25 2.91

6-9 713899 6 -0.81 -0.03 -0.78 0.17 0.01 0.42 -1.85

9-12 700980 1 -2.59 0.02 -2.60 1 0.01 1.00 -2.59

12-22 609628 0 0 0.03 0 0 0.01 0 0

16-22 490449 0 0 0.02 0 0 0.01 0 0

22-76 324562 0 0 0.01 0 0 0.01 0 0

Sum 3910407 74

Plan Curvature

Concave 1739700 26 -0.24 -0.22 -0.01 0.04 0.01 0.23 -0.05

Flat 1326391 43 0.54 -0.50 1.04 0.02 0.02 0.20 5.15

Convex 844316 5 -1.16 -0.02 -1.15 0.20 0.01 0.46 -2.50

Sum 3910407 74

TWI 3-6 1193983 2 -2.42 0.03 -2.45 0.50 0.01 0.71 -3.44

6-8 1437546 26 -0.05 -0.24 0.19 0.04 0.01 0.23 0.84

8-18 1278878 46 0.64 -0.55 1.20 0.02 0.02 0.20 5.99

Sum 3910407 74

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Table 2. Continue

Parameter class

No. of

pixels in

domain

No. of

well W+ W- C

S2

(W+) S2 (W-) S(C) C/S(C)

Land use Farming 1509643 70 0.90 -1.12 2.01 0.01 0.03 0.22 9.33

Rangeland 833535 1 -2.76 0.02 -2.78 1.00 0.01 1.00 -2.77

Garden 14053 0 0 0 0 0 0.01 0 0

Forest 1532934 3 -2.27 0.03 -2.30 0.33 0.01 0.59 -3.93

City 8070 0 0 0 0 0 0.01 0 0

Woodland 12172 0 0 0 0 0 0.01 0 0

Sum 3910407 74

Soil Entisols 650189 5 -0.90 -0.02 -0.88 0.20 0.01 0.46 -1.91

Inceptisolds 3249736 69 0.12 -1.01 1.12 0.01 0.03 0.21 5.24

Vertisols 10482 0 0 0 0 0 0.01 0 0

Sum 3910407 74

Lithology Cenozoic 2722644 70 0.31 -1.06 1.37 0.01 0.03 0.22 6.35

Mesozoic 1119545 4 -1.67 0.01 -1.67 0.25 0.01 0.51 -3.28

Cenozoic-

Mesozoic 68218 0 0 0 0 0 0.01 0 0

Sum 3910407 74

Drainage

density(km/

km2)

0.00 1611590 4 -2.03 0.03 -2.06 0.25 0.01 0.51 -4.04

0-0.11 586797 6 -0.62 -0.04 -0.58 0.17 0.01 0.42 -1.37

0.11-0.21 572194 7 -0.44 -0.05 -0.39 0.14 0.01 0.39 -0.99

0.21-0.31 579002 24 0.78 -0.25 1.03 0.04 0.01 0.23 4.41

0.31-.68 560824 33 1.13 -0.37 1.51 0.03 0.01 0.21 7.10

Sum 3910407 74

Distance

from river(m)

0-806 783745 42 1.04 -0.50 1.55 0.02 0.02 0.20 7.66

806-1714 781685 21 0.35 -0.20 0.55 0.05 0.01 0.25 2.25

1714-2765 782574 7 -0.75 -0.04 -0.71 0.14 0.01 0.39 -1.81

2765-4057 781365 3 -1.60 0 -1.60 0.33 0.01 0.59 -2.73

4057-9525 781038.0

0 1 -2.69 0.02 -2.72 1.00 0.01 1.00 -2.70

Sum 2344977.

00 11

Fault

density(km/km2)

0 2099785.

00 22 -0.59 -0.16 -0.44 0.05 0.01 0.24 -1.81

0-0.16 599336.0

0 28 0.90 -0.30 1.20 0.04 0.01 0.22 5.41

0.16-0.33 605105.0

0 23 0.70 -0.23 0.93 0.04 0.01 0.24 3.92

0.33-1.12 606181.0

0 1 -2.44 0.02 -2.46 1.00 0.01 1.00 -2.44

Sum 3910407.

00 74

Fault

distance(m) 0-710 725592.0

0 24 0.56 -0.24 0.80 0.04 0.01 0.23 3.42

2710-2051 846793.0

0 22 0.32 -0.21 0.53 0.05 0.01 0.24 2.19

2051-4971 788600.0

0 20 0.29 -0.19 0.48 0.05 0.01 0.25 1.92

4971-9390 780598.0

0 6 -0.90 -0.03 -0.87 0.17 0.01 0.42 -2.07

9390-20122 768824.0

0 2 -1.98 0.01 -2.00 0.50 0.01 0.71 -2.80

Sum 3910407 74

Table 3 Effect of influencing factor, relative rates and score for each potential factor in MIF method

Parameter Altitude Slope

aspect

slope

angle

Plan

curvature TWI

Land

use

Drainage

density

Distance

from river

Fault

density

Fault

distance Lithology Soil Sum

Altitude 0 0 0 0 0 0 0 0 0 0 0 0

Slope aspect 0 0 0 0.5 0 0 0 0 0 0 0.5 0

slope angle 0.5 0 0 1 0 0.5 0 0 0.5 0 0.5 0.5

Plan curvature 0.5 0 0.5 0 0.5 0 0.5 0 0 0.5 0.5 0

TWI 1 0.5 1 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5

Land use 1 0.5 1 0 1 0 0.5 0.5 1 0.5 0.5 1

Drainage density 0.5 0 0.5 0.5 1 0.5 0 0.5 0.5 1 0.5 0.5

Distance from river 0 0 0 0 1 0 0.5 0 0 0 0.5 0

Fault density 0 0 0 0 0 0 0 0 0 0.5 0 0

Fault Distance 0 0 0 0 0 0 0 0 1 0 0 0

Lithology 0.5 0 0 0 0 0 0 0.5 0 0.5 0 0

soil 0 0 0 0 0.5 1 0.5 0 0 0 0.5 0

Sum 4 1 3 2 4 2 2.5 2 3.5 3.5 4 2.5 34

Final weight 11.76 2.94 8.82 5.88 11.76 5.88 7.35 5.88 10.29 10.29 11.76 7.35

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Table 4 Classification of weighted factors influencing the potential zones in MIF method

Parameter class No. of well Percentage

of wells Weightage

932-1524 13 17.57 5

Altitude (m) 1524-1722 0 0 1

1722-1859 35 47.30 12

1859-2000 26 35.14 9

2000-3243 0 0 1

Sum 74 100 28

Slope aspect Flat 2 2.70 1

N 7 9.46 2

NW 7 9.46 2

E 6 8.11 2

ES 6 8.11 2

S 8 10.81 2

SW 20 27.03 3

W 12 16.22 3

N 6 8.11 2

Sum 72 97.30 19

Slope angle(in degree) 0-4 47 63.51 9

4-6 20 27.03 5

6-9 6 8.11 3

9-12 1 1.35 2

12-22 0 0 1

16-22 0 0 1

22-76 0 0 1

Sum 74 100 22

Plan Curvature Concave 26 35.14 4

Flat 43 58.11 6

Convex 5 6.76 1

Sum 74 100 11

TWI 3-6 2 2.70 1

6-8 26 35.14 7

8-18 46 62.16 12

Sum 74 100 20

Land use Farming 70 94.59 6

Rangeland 1 1.35 1

Garden 0 0 1

Forest 3 4.05 2

City 0 0 1

Woodland 0 0 1

Sum 74 100 12

Soil Entisols 5 6.76 2

Inceptisolds 69 93.24 7

Vertisols 0 0 1

Sum 74 100 10

Lithology Cenozoic 70 94.59 12

Mesozoic 4 5.41 2

Cenozoic-Mesozoic 0 0 1

Sum 74 100 15

Drainage density(km/km2) 0-0.11 6 8.11 2

0.11-0.21 7 9.46 3

0.21-0.31 24 32.43 6

0.31-.68 33 44.59 7

Sum 70 94.59 18

Distance from river(m) 0-806 42 56.76 6

806-1714 21 28.38 5

1714-2765 7 9.46 3

2765-4057 3 4.05 2

4057-9525 1 1.35 1

Sum 74 100 17

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Table 4:. Continue

Parameter class No. of well Percentage

of wells Weightage

Fault density 0-0.16 28 37.84 10

0.16-0.33 23 31.08 5

0.33-1.12 1 1.35 1

Sum 74 100 23

Fault distance(m) 0-710 24 32.43 10

2710-2051 22 29.73 9

2051-4971 20 27.03 8

4971-9390 6 8.11 3

9390-20122 2 2.70 1

Sum 74 100 31

Table 5 Geo unites of study area

Age_ERA Geounit AGE DISCRIPTION

CENOZOIC Qft2 Quaternary Low level piedment fan and vally terrace deposits

CENOZOIC PlQc

Pliocene-

Quaternary Fluvial conglomerate, Piedmont conglomerate and sandstone.

CENOZOIC OMql Oligocene-Miocene Massive to thick - bedded reefal limestone

CENOZOIC Qft1 Quaternary High level piedmont fan and vally terrace deposits

CENOZOIC Plc Pliocene Polymictic conglomerate and sandstone

CENOZOIC PeEf

Paleocene-

Eocene Flysch turbidite, sandstone and calcareous mudstone

CENOZOIC OMq

Oligocene-

Miocene Limestone, marl, gypsiferous marl, sandymarl and sandstone (QOM FM )

CENOZOIC E2c Middle.Eocene Conglomerate and sandstone

CENOZOIC MuPlaj Miocene Brown to grey, calcareous, feature-forming sandstone and low weathering, gypsum- veined, red marl and siltstone (AGHAJARI FM)

CENOZOIC Plbk Pliocene

Alternating hard of consolidated, massive, feature forming conglomerate and low -weathering

cross -bedded sandstone ( BAKHTYARI FM)

CENOZOIC EMas-sb Eocene-Miocene Undivided Asmari and Shahbazan Formation

CENOZOIC Ekn Eocene Tine-bedded argillaceous limestone and calcareous shale (Kandavan Shale)

CENOZOIC MPlfgp Miocene

FARS GROUP comprising the following formation Gachsaran, Mishan and Aghajari, (Reefal

Coral and Algal Limestone)

CENOZOIC E Eocene Undivided Eocene rocks

CENOZOIC Ekn Eocene Tine-bedded argillaceous limestone and calcareous shale (Kandavan Shale )

CENOZOIC Mgs Miocene Anhydrite, salt, grey and red marl alternating with anhydrite, argillaceous limestone and limestone ( GACHSARAN FM )

MESOZOIC Kbgp Cretaceous

Undivided Bangestan Group, mainly limestone and shale, Albian to Companian, comprising the

following formations: Kazhdumi, Sarvak, Surgah and Ilam

MESOZOIC Kgu Cretaceous Bluish grey marl and shale with subordinate thin - bedded argillaceous -limestone ( GURPI FM )

MESOZOIC Kur Late.Cretaceous Radiolarian chert and shale

MESOZOIC K1bl Cretaceous Grey, thick - bedded to massive o'olitic limestone

MESOZOIC TRKurl

Triassic-

Cretaceous

Purple and red thin - bedded radiolarian chert with intercalations of neritic and pelagic limestone

(Kerman and Neyzar Radiolarites )

MESOZOIC JKbl Jurassic-Cretaceous Grey, thick - bedded, o'olitic, fetid limestone

MESOZOIC pd

Triassic-

Cretaceous Peridotite including harzburgite, dunite, lerzolite and websterite

MESOZOIC Klsol Early.Cretaceous Grey thick - bedded to massive orbitolina limestone

MESOZOIC TRKubl

Triassic-

Cretaceous Kuhe Bistoon limestone

MESOZOIC TRJlr Triassic-Jurassic Grey, thin to thick bedded, partly cherty, neritic limestone intercalation of radiolarian shale and chert

MESOZOIC Kbgp Cretaceous

Undivided Bangestan Group, mainly limestone and shale, Albian to Companian, comprising the

following formations: Kazhdumi, Sarvak, Surgah and Ilam

MESOZOIC TRJvm Triassic-Jurassic Meta - volcanic, phyllites, slate and meta- limestone

MESOZOIC-CENOZOIC KPeam

Cretaceous-Paleocene

Dark olive - brown, low weathered siltstone and sandstone with local development of chert conglomerates and shelly limestone (AMIRAN FM )

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59


4 Conclusions Evaluation of groundwater potential is an

essential tool for water foundation

management and land use planning. In actual,

politician and plan manager scan use this tool

for water resource management. In this

research, groundwater potential maps have

been created using FR, WoE and MIF

method. In the first step, influence layers were

prepared in ArcGIS10.2 software. These

layers were altitude, slope aspect, slope angle,

plan curvature, Topographic Wetness Index

(TWI), land use, soil, lithology, drainage

density, distance from river, fault density and

fault distance. Then, after classified the layers

to different classes, the FR, WoE and MIF

methods were applied for groundwater

potential mapping. After that, final map was

classified to five classes in all three methods.

The result showed that, the altitude class of

1722-1859 m, the slope class of 0-6 degree,

all aspect classes specially southwest slope

aspect, concave and flat plan curvatures, TWI

class of 8-18, farming land use type,

Inceptisols soil type, lithological Cenozoic

class, river density class of 0.31-0.68 (km-1),

distance from river class of 0-710 (m), and

faults were good places for the recharge in the

area. In the second step, the ROC curve and

AUC were prepared to determine the accuracy

of three models. In the study area, 106

groundwater data wells locations were used,

74 (70%) as training and 32 (30%) remaining

wells as testing data. The testing data was

used for the models validation. Calculated

AUC for the three models showed almost the

same accuracy for the three maps, and its high

values indicated that all models can be

successfully applied in groundwater potential

mapping.

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