Available online at www.sciencedirect.

com

ScienceDirect
Aquatic Procedia 4 (2015) 1322 – 1330

INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN

ENGINEERING (ICWRCOE 2015)

Prioritization of Sub Watershed Based on Morphometric

Characteristics Using Fuzzy Analytical Hierarchy Process and
Geographical Information System – A Study of Kallar Watershed,
Tamil Nadu
S. Abdul Rahamana* S. Abdul Ajeezb S.Aruchamyc R.Jegankumard
a
Department of Geography, School of Geosciences, Bharathidasan University, Tiruchirappalli, 620024, India
b
Research and Development, Lumina Datamatics, Chennai, 600034, India
c
Department of Geography, School of Geosciences, Bharathidasan University, Tiruchirappalli, 620024, India
d
Department of Geography, School of Geosciences, Bharathidasan University, Tiruchirappalli, 620024, India

Abstract

Watershed prioritization has gained importance in natural resources management, especially in the context of watershed
management. Delineation of watersheds within a large drainage basin and their prioritization is required for proper planning and
management of natural resources for sustainable development. Delineation of potential zones for implementation of conservation
measures above the entire watershed at similar occurrence is inaccessible as well as uneconomical; therefore it is a prerequisite to
apply viable technique for prioritization of sub watersheds (SW). The present research attempted to study various morphological
characteristics and to implement Geographical Information System (GIS) and Multi Criteria Decision Making (MCDM) through
Fuzzy Analytical Hierarchy Process (FAHP) techniques for identification of critical sub watersheds situated in transaction zone
between mountainous and water scarcity region of kallar watershed, Tamil Nadu. The morphometric characterization was
obtained through the measurement of three distinct linear, areal and relief aspects over the eleven sub-watersheds. The
morphometric characterization showed vital role in distinguishing the topographical and hydrological behavior of the watershed.
Each morphometric parameters were ranked with respect to the value and weightings obtained by deriving the relationships
between the morphometric parameters obtained through classification of the SW by associating the strength of Fuzzy Analytical
Hierarchy Processes (FAHP). Based on FAHP approach, sub watersheds were evaluated and divided into five prioritization
zones: very less, less, medium, high and very high classes. The FAHP techniques is a practical approach for identification of the

* Corresponding author. Tel.: 09787914144; fax: +0-000-000-0000

E-mail address: abdulatgeo@gmail.com

2214-241X © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of organizing committee of ICWRCOE 2015
doi:10.1016/j.aqpro.2015.02.172
 S. Abdul Rahaman et al. / Aquatic Procedia 4 (2015) 1322 – 1330 1323

sensitive priority zones and is useful for better management practices such as implementation of land and water resource
management, conservation and sustainable agricultural development.

© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
© 2015 The Authors. Published by Elsevier B.V.
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under
Peer-review under responsibility
responsibility of organizing
of organizing committee
committee of ICWRCOE
of ICWRCOE 2015 2015.

Keywords: Prioritization, Morphometric, Fuzzy Analytical Hierarchy Process, Geographic Information System

1. Introduction

A drainage basin or watershed is an extent or an area of land where surface water from rain, melting snow, or ice
converges to a single point at a lower elevation, usually the exit of the basin, where the waters join another water
body, such as a river, lake, reservoir, estuary, wetland, sea, or ocean. The water shed plays a dominant role in the
development of landforms and therefore, the study of drainage basin has a great significance in geomorphic studies.
A watershed is an ideal unit for management of Natural resources like land and water and for mitigation of the
impact of natural disasters for achieving sustainable development. The watershed management concept recognizes
the interrelationships among the linkages between uplands, low lands, land use, geomorphology, slope and soil.
Morphometry is the measurement and mathematical analysis of the configuration of the earth's surface, shape and
dimension of its landforms (Agarwal, 1998; Obi Reddy et al., 2002). A major emphasis in geomorphology over the
past several decades has been on the development of quantitative physiographic methods to describe the evolution
and behaviour of surface drainage networks (Horton, 1945; Leopold & Maddock, 1953). Morphometric analysis of a
watershed provides a quantitative description of the drainage system, which is an important aspect of the
characterization of watersheds (Strahler, 1964).
Pioneering work on the drainage basin morphometry has been carried out by Horton (1932, 1945), Miller (1953),
Smith (1950), Strahler (1964) and others. In India, some of the recent studies on morphometric analysis using
remote sensing technique were carried out by Natuiay (1994), Srivastava (1997), Nag (1998) and Srinivasa et al
(2004). In the earlier researches, prioritization of watershed was accomplished through different approaches to
instance soil erosion or sediment yield indexing (SYI), morphological characterization, socio-economic aspects, etc.
some other studies focused on soil erosion and SYI modelling aspects by classifying the erosion affected priority
areas (Suresh et al., 2004; Ratnam et al., 2005; Kalin and Hantush, 2009; Pandey et al., 2009; Niraula et al., 2011;
Pai et al., 2011). Land deterioration as well as land use change impacts were also measured for evaluation of
prospective zones of watersheds (Adinarayana, 2003; Deb and Talukdar, 2010; Kanth and Hassan, 2010; Javed et
al., 2011; Sarma and Saikia, 2011).
The remote sensing and GIS technique is a convenient method for morphometric analysis as the satellite images
provides a synoptic view of a large area and is very useful in the analysis of drainage basin morphometry. GIS and
remote sensing (RS) techniques are proved to be proficient tools for morphometric characterization of sub-
watersheds (Singh, 1994; Grohmann, 2004; Sreedevi et al., 2009; Aher et al., 2010; Rao et al., 2011). Mishra et al.
(2007) carried out prioritization of sub-watersheds through morphological characteristics by using Soil and Water
Assessment Tool (SWAT) model in the small multi-vegetated watershed of a sub-humid subtropical region in India.
In recent research on prioritization of watersheds using multi-criteria evaluation through fuzzy analytical
hierarchy process (Aher, P. D., 2013). Assessment of different vulnerability producing factors is the decision
making process associated with formation of system knowledge database which involves multiple criteria and
alternatives, which results in great degree of complexity. Therefore, in this research an attempt has been made for
prioritization of sub-watersheds through analysis of the natural drainage system that implements a novel approach
by investigating the fuzzy analytical hierarchy process (FAHP) to circumvent the complex information associated
with various morphological characteristics by accomplishing better accuracy in identification and prioritization of
sub-watersheds with earlier approaches.
1324 S. Abdul Rahaman et al. / Aquatic Procedia 4 (2015) 1322 – 1330

2. Study Area and Data products

The Kallar watershed situated in Eastern slope of Western Ghats stretching from West to the East. It is located
between 11˚17’0” to 11˚31’0” N Latitude and 76˚ 39’ 0” to 77˚ 8’ 45” E Longitude and covers an area of 1281.24
Sq.Km. It comprises of three districts The Nilgiris, Coimbatore and Erode, covering 6 taluks (Coonoor, Kothagiri,
Udhagamandalam, Mettupalayam, Coimbatore north, and Sathyamangalam); it consists of 89 Revenue villages (Fig.
1. (a). The maximum and minimum elevation encountered in the watershed about 177m and 2615m above MSL
(Fig. 1. (b). About 50% of areas are mountains covered with diverse plant communities that form various types of
forest and other agricultural activities, especially Tea, coffee plantation, vegetables and orchards, which are
normally cultivated in the upper and the lower area. The climate of this area is temperate and salubrious for more
than half of the year. The average day temperature of the sub watershed is 16.15° C and the average rainfall is about
901.65mm. The winter is relatively cool. The maximum rainfall is received during the month of October and
November. The Kallar streams flow from Southwest to Northeast and it connects the Bhavani River, which finally
joins with Bhavanisagar Dam, it was built in the Northeastern part of the watershed. Which is primarily serves as
source of irrigation. The area represent by clay soil, loam soil and rock out creep on steep to narrow sloping
landform. Geologically, the area represented by charnockite and fissile hornblende-biotite gneiss. The present study
area falls under the Survey of India toposheets (1:50,000 scale) 58 A/11, 15, 16 and 58E/3 & 4. The watershed and
sub watershed boundary has been demarcated using this drainage network.

Fig. 1. (a) Study area map, (b) Elevation Map

3. Methodology

3.1. Morphometric Analysis

Watershed is a natural hydrological unit, topographically delineated area drained by a stream system, from which
runoff resulting from precipitation flow past from a point into single stream. The drainage network was derived from
Survey of India toposheet, and the sub watershed boundaries were demarcated on the basis of contour and drainage
flow direction. The Kallar watershed was divided into ten sub watersheds named as SW1 to SW10 (Fig. 2. (a) and
(b)). The smallest (SW9) and the largest (SW1) sub watersheds measures 40.9 and 210 Sq.km, respectively. The
morphometric parameters broadly classified into three aspects, viz... linear, areal, and relief, the sub parameters such
as stream order, number of streams, stream length, bifurcation ratio, drainage density, stream frequency, drainage
texture, relief ratio, basin shape, form factor, circularity ratio, elongation ratio, and length of overland flow.
The naming of stream order is the first step in morphometric analysis of drainage basin, based on the hierarchic
of stream proposed by straheler (1964). In the study area SW9 and SW10 are of forth order, SW4, SW5, SW6, and
SW8 are of fifth order, SW1, SW2, and SW3 are of sixth order, whereas , SW7th have seventh order. The entire
Kallar watershed is falls under seventh order. The morphometric characterization in the form of linear, areal and
relif aspects for the delineated sub watershed was calculated based on given formula (Table 1 & 2).

Table 1. Formula for computation of morphometric parameters.

 S. Abdul Rahaman et al. / Aquatic Procedia 4 (2015) 1322 – 1330 1325

Sl. No. Morphometric Parameters Formula/ Definition/Methods Reference

Linear aspect:
1. Stream order Hierarchical order Strahler 1964
2. Stream Length (Lu) Length of the Stream (km) Horton 1945
Lsm = Lu / Nu, Where, Lu=Total Stream length of order ‘U’, Nu=
3. Mean Stream Length (Lsm) Horton 1945
Stream length of next higher stream order.
Rl=Lu/Lu-1; where, Lu= Total number of stream segment of order
4. Stream length Ratio(Rl) Horton 1945
‘U’, Lu-1=Stream length of next lower order.
Rb = Nu / Nu+1
5. Bifurcation Ratio (Rb) Where, Nu=Total Number of stream segments of order ‘U’,
Nu+1= Number of segments of the next higher order. Schumm 1956
Arial Aspect:
Dd =L/A
6. Drainage Density (Dd) Where, L=Total length of Stream, Horton 1945
A=Area of the Watershed.
Fs = N /A. Where, N= Total number of Stream,
7. Stream Frequency (Fs) Horton 1945
A= Area of the Watershed.
T=N1/P. Where N1= Total number of first order stream, p=
8. Texture ratio (T) Horton 1945
perimeter of watershed.
Rf = A / (Lb) 2. Where, A=Area of the Watershed, Lb=Maximum
9. Form Factor (Rf) Horton 1932
Basin length.
Rc = 4ðA/ P2. Where, A=Area of the Watershed,
10. Circularity Ratio (Rc) Miller 1953
P= Perimeter of the basin, Ð = 3.14
Re= 2√ (A/ð) / Lb. Where, A=Area of the Watershed,
11. Elongation Ratio (Re) Schumm 1956
Lb=Maximum Basin length, Ð = 3.14.
12. Length of Over Land Flow (Lof) Lof = 1/ 2Dd. Where, Dd = Drainage density. Horton 1945
13. Constant channel maintenance 1/dd. Where, Dd=Drainage Density. Horton 1945
Relief Aspects:
Vertical Distance between the lowest and highest point of water
14. Basin Relief(Bh) Schumm 1956
shade.
15. Relief Ratio (Rh) Rh=Bh/Lb; where, Bh=Basin Relief, Lb= Basin length. Schumm 1956
Rn=Bh*Dd; Where, Bh=Basin Relief,
16. Ruggedness number(Rn) Schumm 1956
Dd= Drainage Density.

Table 2. Results of the morphometric analysis of the sub watershed

Kallar
Morphometric Parameters SW1 SW2 SW3 SW4 SW5 SW6 SW7 SW8 SW9 SW10
Watershed
No. of Stream 1073 1103 400 823 500 355 458 500 191 657.00 6098
Stream Length (Lu) m 568 544 221 423 314 239 309 314 102 414.67 3438.89
Bifurcation Ratio (Rb) I/ II 2.17 2.4 2.04 1.85 1.78 1.91 1.83 1.78 2.04 1.97 2.05
Bifurcation Ratio (Rb) II/ III 2.39 1.84 1.85 2.34 3.25 2.77 2.35 3.25 2.78 2.20 2.32
Bifurcation Ratio (Rb) III/ IV 2.24 1.75 2.75 3.23 1.26 1.21 5.18 1.26 0.86 3.65 2.08
Bifurcation Ratio (Rb) IV/ V 0.96 1.04 2 1.24 1.52 3.22 - 1.52 - 23.00 1.47
Bifurcation Ratio (Rb) V/ VI 1.76 14.4 2 - - - - - - 5.69
Bifurcation Ratio (Rb) VI/ VII - - - - - - - - 3.55
Mean Bifurcation Ratio (Rbm) 1.91 4.29 2.13 2.16 1.95 2.28 2.59 1.59 1.89 7.71 2.85
Stream length Ratio (Rl) II/ I 3.3 3.97 3.11 2.7 2.51 1.95 2.49 2.51 2.95 2.44 2.85
Stream length Ratio (Rl) III/ II 2.2 1.67 1.67 1.9 2.93 3.04 1.76 2.93 1.8 1.82 2.04
Stream length Ratio (Rl) IV/ III 2.29 1.92 1.81 2.68 1.13 1.2 4.29 1.13 0.84 2.25 1.97
Stream length Ratio (Rl) V/ IV 0.85 1.27 1.59 1 0.9 2.1 0.42 0.9 - 0.88 1.51
Stream length Ratio (Rl) VI/ V 1.87 5.27 1.94 - - - - - - - 5.32
Stream length Ratio (Rl) VII/ VI - - - - - - - - - - 0.46
Perimeter (P) (Km) 74.6 78.1 50.4 61.4 61.9 49.5 54.2 61.9 32.5 94.94 212.48
Area (A) (Sq.Km) 210 172 85.1 122 120 112 126 120 40.3 194.58 1293.15
Stream Frequency (Fs) 5.1 6.41 4.7 6.73 4.17 3.18 3.63 4.17 4.73 3.38 4.72
Drainage Texture (Dt) 14.4 14.1 7.94 13.4 8.07 7.17 8.45 8.07 5.87 6.92 28.70
Drainage Density (Dd) 2.7 3.16 2.59 3.46 2.62 2.14 2.45 2.62 2.53 2.13 2.66
Compactness Coefficient (Cc) 1.46 1.69 1.55 1.58 1.61 1.33 1.37 1.61 1.46 1.93 1.68
Length of Basin (Lb) (Km) 27.4 24.4 16.4 20.1 19.9 19.1 20.5 19.9 10.7 26.19 76.80
Basin shape (Bs) 0.28 0.29 0.32 0.3 0.3 0.31 0.3 0.3 0.35 0.28 0.22
Length of overland flow (Lof) (Km) 0.18 0.16 0.19 0.14 0.19 0.23 0.2 0.19 0.2 0.23 0.19
1326 S. Abdul Rahaman et al. / Aquatic Procedia 4 (2015) 1322 – 1330

Constant of Channel Maintenance(C) 0.37 0.32 0.39 0.29 0.38 0.47 0.41 0.38 0.4 0.47 0.38
Lemniscate’s (k) 3.56 3.47 3.15 3.31 3.3 3.27 3.32 3.3 2.85 3.53 4.56
Circularity Ratio (Rc) 0.47 0.36 0.42 0.41 0.39 0.57 0.54 0.39 0.48 0.27 0.36
Form Factor Ratio (Rf) 0.28 0.29 0.32 0.3 0.3 0.31 0.3 0.3 0.35 0.28 0.22
Elongation Ratio (Re) 0.29 0.32 0.48 0.39 0.4 0.41 0.39 0.4 0.74 0.30 0.10
Shape Factor Ratio (Rs) 3.56 3.47 3.15 3.31 3.3 3.27 3.32 3.3 2.85 3.53 4.56
Min Elevation (h) (M) 293 291 302 327 310 273 247 258 251 177 177
Max Elevation (H) (M) 2614 2242 812 1578 1499 450 1369 2116 581 1887 2615
Total Basin Relief (R) (m ) 2321 1951 510 1251 1189 177 1122 1858 330 1710.00 2438
Relief Ratio (Rr) 84.8 79.9 31.2 62.2 59.8 9.26 54.8 93.4 30.8 65.29 31.75
Relative Relief (RhP) 31.1 25 10.1 20.4 19.2 3.58 20.7 30 10.1 18.01 11.47
Ruggedness number (Rn) 6274 6159 1323 4329 3115 378 2747 4868 834 3644.19 6483.40
Gradient Ratio (Rg) 84.8 79.9 31.2 62.2 59.8 9.26 54.8 93.4 30.8 65.29 31.75
Melton Ruggedness Number (MRn) 160 149 55.3 113 109 16.7 99.9 170 52 122.59 67.80
Texture ratio (T) 7.65 7.42 4.13 7.05 4.12 3.74 4.52 4.12 3.14 3.83 15.25

Fig. 2. (a) Drainage map, (b) Sub watershed

4. Prioritization of Sub Watershed using FAHP

In order to prepare the prioritize watershed, various methods such as quantitative, fuzzy logic, statistic methods
and AHP where used by several researches. The present study of Fuzzy Analytical Hierarchical Process (FAHP)
with extent analysis method was used to prioritize the watersheds. The Analytical Hierarchy Process (AHP), which
is a multiple criteria decision making tools, especially in the problems with spatial nature or GIS-based. In addition
AHP, its manner to apply and its weaknesses and strengths and ultimately the fuzzy modified Analytical Hierarchy
Process (FAHP) which is proposed after that the concepts like of fuzziness, uncertainty and vagueness was broadly
posed in expert's decision making. Fuzzy AHP is described to obtain a crisp priority vector from a triangular fuzzy
comparison matrix. In spite of popularity of AHP, this method is often criticized for its inability to adequately
handle the inherent uncertainty and imprecision associated with the mapping of the decision maker’s perception to
exact numbers (Deng, 1999).
To apply the process depending on this hierarchy, according to the method of extent analysis, each criterion is
taken and extent analysis for each criterion, gi; is performed on, respectively. Therefore, m extent analysis values for
each criterion can be obtained by using following notation.
M 1gi , M gi2 , M gi3 , M gi4 , M gi5 ,}}, M gin Where gi is the goal set (i = 1, 2, 3, 4, 5 … n) and all the M gij (j = 1, 2, 3, 4,
5…m) are Triangular Fuzzy Numbers (TFNs). The steps of Chang’s analysis can be given as in the following:
Step 1: The fuzzy synthetic extent value (Si) with respect to the ith criterion as follows:
 S. Abdul Rahaman et al. / Aquatic Procedia 4 (2015) 1322 – 1330 1327

1
m ªn m º
Si ¦M j
gi «¦ ¦ M » j
gi (1)
j 1 ¬i 1 j 1 ¼
This involves computation of
m

¦M
j 1
j
gi (2)

Perform the “fuzzy addition operation” of m extent analysis values for a particular matrix given in (3) below, at
the end step of calculation, new (l, m, u) set is obtained and used for the next:
m m m m

¦M
j 1
j
gi (¦ l j , ¦ m j , ¦ u j )
j 1 j 1 j 1
(3)

Where “l” is the lower limit value, “m” is the most promising value and “u” is the upper limit value and to obtain
(4); this involves computation of
1
ª n m º
«¦¦ M gi »
j
(4)
¬i 1 j 1 ¼
Perform the “fuzzy addition operation” of M gij (j = 1, 2, 3, 4, 5…m) values given
n m m m m

¦¦ M
i 1 j 1
j
gi (¦ li , ¦ mi , ¦ ui )
j 1 j 1 j 1
(5)

and then compute the inverse of the vector in (5) . (6) Is then obtained such that:
ª º
1
ª n m º « 1 1 1 »
«¦¦ M gi » « n , n , n »
j
(6)
« u »
«¬ ¦ i ¦ mi ¦ li »
¬i 1 j 1 ¼
i 1 i 1 i 1 ¼
Step 2: The degree of possibility of M 2 (l 2 , m2 , n2 , ) t M 1 (l1 , m1 , n1 , ) is defined as equation (7):

V (M 2 t M1 ) sup>min( P M 1 ( x), ( P M 2 ( y))@ (7)

yt x

and x and y are the values on the axis of membership function of each criterion. This expression can be equivalently
written as given in equation (8) below:
­
° 1, if
° m2 t m1 ,
° (8)
V (M 2 t M 1 ) ® 0, if m2 t m1,
°
° l1  u 2
° (
¯ 2 m  u 2)  ( m1  l1)

Step 3. The degree possibility for a convex fuzzy number to be greater than k convex fuzzy numbers Mi (i = 1, 2,
3, 4, 5 … k) can be defined by
V ( M t M 1 , M 2 , M 3 ,}} M k )
V [( M t M 1 ) and ( M t M 2 ) and ( M t M 3 ) and }} and ( M t M k ) (9)
min V ( M t M i ), i 1,2,3,4,} k
Assume that equation (9) is:
1328 S. Abdul Rahaman et al. / Aquatic Procedia 4 (2015) 1322 – 1330

d ( Ai ) minV ( S i t S k )
For k = 1, 2, 3, 4, 5…n; k≠ i. Then the weight vector is given by equation (10):
W1 (d ( A1 ), d ( A2 ), d ( A3 ), d ( A4 ), d ( A5 )}d ( An ))T (10)
Where Ai (i = 1, 2, 3, 4, 5, 6… n) are n elements.
Step 4. Via normalization, the normalized weight vectors are given in (11):
W (d ( A1 ), d ( A2 ), d ( A3 ), d ( A4 ), d ( A5 )}d ( An ))T (11)
Where W is non-fuzzy numbers.

5. Result and Discussion

The morphometric parameters i.e., bifurcation ratio (Rb), drainage density (Dd), stream frequency (Fs), drainage
texture (Dt), circularity ratio (Rc), relief ratio (Rr), ruggedness number (Rn) basin relief (Rh) and basin shape (Bs)
are also termed as erosion risk assessment parameters and have been used for prioritizing watersheds. The linear
parameters such as drainage density, stream frequency, bifurcation ratio, drainage texture, have a direct relationship
with erodibility, higher the value, more is the erodibility. Hence the prioritization of sub watershed, the highest
value was rated as score or rank based on Triangular Fuzzy Numbers (TFN) as very strong importance (VSI -
2,5/2,3), second highest value was rated as strongly importance (SI – 3/2,2,5/2), and so on, the least value was rated
as weakly more importance (WMI-1,3/2,2), and both parameters meet an same influence was rated as just equal (JE
– 1,1,1). Shape parameter such as circularity ratio, basin shape and compactness coefficient have an inverse
relationship with erodibility, lower the value of shape parameters was rated as very strong importance (VSI), nest
lower value was rated as ranked strongly importance (SI – 3/2,2,5/2), the highest value was rated as weakly more
importance (WMI-1,3/2,2), and both parameters meet an same influence was rated as just equal (JE – 1,1,1), sub
watershed wise morphometric variables used for pair-wise comparison matrix in fuzzy analytical hierarchy process
to generate the morphometric criterion weight. Same way each morphometric parameters compare with every sub
watershed (Table.3 & 4).

Table 3. Morphometric properties matrix of Kallar watershed

Stream Drainage Drainage Ruggedness Basin Basin
Mean bifurcation Circularity Relief
Frequency Texture Density number Relief (R) Shape
Ratio(Br) Ratio (Rc) Ratio (Rr)
(Fs) (Dt) (Dd) (Rn) m (Bs)
SW1 1.91 5.10 14.38 2.70 0.47 0.08 6.27 2321.00 0.28
SW2 4.29 6.41 14.13 3.16 0.36 0.08 6.16 1951.00 0.29
SW3 2.13 4.70 7.94 2.59 0.42 0.03 1.32 510.00 0.32
SW4 2.16 6.73 13.40 3.46 0.41 0.06 4.33 1251.00 0.30
SW5 1.95 4.17 8.07 2.62 0.39 0.06 3.12 1189.00 0.30
SW6 2.28 3.18 7.17 2.14 0.57 0.01 0.38 177.00 0.31
SW7 2.59 3.63 8.45 2.45 0.54 0.05 2.75 1122.00 0.30
SW8 1.59 4.17 8.07 2.62 0.39 0.09 4.87 1858.00 0.30
SW9 1.89 4.73 5.87 2.53 0.48 0.03 0.83 330.00 0.35
SW10 7.71 3.38 6.92 2.13 0.27 0.07 3.64 1710.00 0.28

Table 4. Pair-Wise matrix of morphometric properties of Kallar watershed

Wc
Br Sf Dd Dt Cr Rn Rh Rr Bs
(0.5, 1, (0.28, 0.33, (0.5, 1, (0.28, 0.33, (0.28, 0.33, (0.28, 0.33, 0.06
Br (1,1,1) (2.5, 3, 3.5) 1.5) 0.4) 1.5) 0.4) 0.4) 0.4) (1, 1, 1)
(0.28, 0.33, (0.33, 0.4, (0.5, 0.67, (0.33, 0.4, (0.4, 0.5, (0.33, 0.4, 0.04
Sf 0.4) (1, 1, 1) (1, 1, 1) 0.5) 1) 0.5) 0.67) (2.5, 3, 3.5) 0.5)
(0.33, 0.4, (0.67, 1, (0.33, 0.4, (0.33, 0.4, 0.07
Dd (0.67, 1, 2) (1,1,1) (1, 1, 1) 0.5) 2) 0.5) (1, 1, 1) (0.67, 1, 2) 0.5)
 S. Abdul Rahaman et al. / Aquatic Procedia 4 (2015) 1322 – 1330 1329

(0.67, 1, (0.33, 0.4, 0.17

Dt (2.5, 3, 3.5) (2, 2.5, 3) (2, 2.5, 3) (1, 1, 1) 2) (0.67, 1, 2) (2, 2.5, 3) 0.5) (1, 1, 1)
(0.5, 1, (0.4, 0.5, 0.11
Cr (0.67, 1, 2) (1, 1.5, 2) 1.5) (0.5, 1, 1.5) (1, 1, 1) (0.67, 1, 2) (0.67, 1, 2) 0.67) (0.67, 1, 2)
(0.4, 0.5, (0.5, 1, (0.33, 0.4, 0.11
Rn (2.5, 3, 3.5) 0.67) (2, 2.5, 3) (0.5, 1, 1.5) 1.5) (1, 1, 1) (0.67, 1, 2) 0.5) (0.67, 1, 2)
(0.33, 0.4, (0.5, 1, 0.15
Rh (2.5, 3, 3.5) (1.5, 2, 2.5) (1, 1, 1) 0.5) 1.5) (0.5, 1, 1.5) (1, 1, 1) (0.67, 1, 2) (2.5, 3, 3.5)
(0.28, 0.33, (0.5, 1, (1.5, 2, 0.20
Rr (2.5, 3, 3.5) 0.4) 1.5) (2, 2.5, 3) 2.5) (2, 2.5, 3) (0.5, 1, 1.5) (1, 1, 1) (2, 2.5, 3)
(0.5, 1, (0.28, 0.33, (0.33, 0.4, 0.11
Bs (1,1,1) (2, 2.5, 3) (2, 2.5, 3) (1,1,1) 1.5) (0.5, 1, 1.5) 0.4) 0.5) (1, 1, 1)

Finally we obtained criterion weight and alternative weight, for prioritization of sub watershed criterion weights
were multiply with alternative weight, and overlaid all sub watershed criterion in GIS environment to prepare a
prioritization of sub watershed. The final score ranges in Fuzzy Analytical Process is 0.074 to 0.167, where highest
rank assigned to the sub watershed having the highest analysis value, the same way ranks were assigned to each sub
watershed (Fig. 3. (a)) In this study SW10 was allotted for highest priority having FAHP value of 0.167 followed by
SW4, SW1, SW7, SW9, SW6, SW3, SW5, SW8, SW2 is the least priority in this watershed (Table. 5).

Table 5. Prioritization ranking of the sub watershed

Sub Watershed Sw1 Sw2 Sw3 Sw4 Sw5 Sw6 Sw7 Sw8 Sw9 Sw10
FAHP Score 0.109 0.074 0.084 0.113 0.082 0.100 0.107 0.080 0.104 0.167
Priority Rank 3 10 7 2 8 6 4 9 5 1

The prioritizes FAHP score was divided in to five priority classes from very less to very high based on the overall
weights(Table.6). The Kallar watershed categorized priority classes, very less priority is SW2, it covers an area of
13.15%, and very high priority class is SW4, SW10, it covers an area of 24.50%. (Fig. 3. (b)). The SW10 falls under
very high priority category, which is located near to the reservoir, it contain high amount of erosion. Hence the
SW10 need immediate concern to protect and control the erosion. The SW1 is falls under the high priority category,
which is located on high altitude area with huge amount of population and this sub watershed falls under the highly
landslide hazard zone, this sub watershed also need immediate action to sustain the environmental condition.

Table 6. FHAP score with different priority class

S.No Priority Score Priority Class Sub Watershed Area in (%)
1 < 0.079 Very Less SW2 13.32
2 0.0791 - 0.085 Less SW3, SW5, SW8 24.39
3 0.0851 - 0.108 Medium SW6, SW7, SW9 21.53
4 0.109 - 0.111 High SW1 16.26
5 > 0.112 Very High SW10, SW4 24.51

Fig. 3. (a) Priority Rank map, (b) Prioritization of sub watershed

1330 S. Abdul Rahaman et al. / Aquatic Procedia 4 (2015) 1322 – 1330

6. Conclusion

The present study demonstrate the utility of Geographical Information System (GIS) and Multi Criteria Decision
Making (MCDM) techniques in prioritizing sub watershed based on morphometric as well as integration of Fuzzy
Analytical Hieratical Process (FAHP) with extent analysis. In this study, a new and consistent approach of MCDM
processes i.e. FAHP analysis based prioritization was formulated successfully which plays a very important role in
illustrating the problem through integration of risk assessment factors causing natural resources. This may be one of
the feasible and efficient techniques, particularly in conventional watershed prioritization approaches for designing
and developing the efficient sustainable development and management practices. In this study, it has found that
SW4 and SW10 are falls in the high priority category based on morphometric analysis; hence, these sub watersheds
may be taken for conservation measurement by decision maker for planning and development.

References

Agarwal CS 1998. Study of drainage pattern through aerial data in Naugarh area of Varanasi district, U.P. Jour Indian Soc Remote Sensing 26:
169-175.
Strahler AN 1964. Quantitative geomorphology of drainage basins and channel networks: Handbook of applied hydrology. McGraw Hill Book
Company, New York, Section 4-76.
Horton, R. E. 1932. Drainage basin characteristics. Am.Geophys. Union, Trans. 13, 348–352.
Biswas S, Sudhakar S, Desai VR 1999. Prioritization of sub-watersheds based on Morphometric Analysis of Drainage Basin, District Midnapore,
West Bengal. J Indian Soc Remote Sensing 27: 155-166.
Nag SK 1998. Morphometric analysis using remote sensing techniques in the Chaka sub-basin, Purulia district, West Bengal. J Indian Soc
Remote Sensing 1: 69-76.
Singh, S. and Singh, M.C. 1997 Morphometric analysis of Kanhar river basin. National Geographical J. of lndia, (43), 1: 31-43.
M. Imran Malik, M. Sultan Bhat and Nissar A. Kuchay., 2011. Watershed based drainage Morphometric Analysis of Lidder catchment in
Kashmir Valley using Geographical Information System. Recent Research in Science and Technology 2011, 3(4): 118-126.
Adinarayana, J. 2003. Spatial decision support system for identifying priority sites for watershed management schemes. In Proc. First Interagency
Conference on Research in the Watersheds (ICRW), 405-408. Benson, Arizona, USA: U. S. Department of Agriculture, Agricultural
Research Service, 27-30 October.
Aher, P. D., J. Adinarayana, and S. D. Gorantiwar. 2013. Prioritization of watersheds using multi-criteria evaluation through fuzzy analytical
hierarchy process. Agric Eng Int: CIGR Journal, 15(1): 11୉18.
Chang, D. Y. 1996. Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3): 649-655.
Saaty, T. L. 1980. The analytic hierarchy process. New York, U.S.: Mc Graw Hill.
Grohmann, C. H. 2004. Morphometric analysis in Geographic Information Systems: applications of free software GRASS and R star. Computers
and Geosciences, 30(10): 1055-1067.
Hlaing, K., S. Haruyama, and A. Maung. 2008. Using GIS-based distributed soil loss modeling and morphometric analysis to prioritize watershed
for soil conservation in Bago river basin of Lower Myanmar. Frontiers of Earth Science in China, 2(4): 465-478.
Javed, A., M. Y. Khanday, and R. Ahmed. 2011. Watershed prioritization using morphometric and land use/land cover parameters: a remote
sensing and GIS based approach. Journal Geological Society of India, 78(1): 63-75.
Mishra, A., S. Kar, and V. P. Singh. 2007. Prioritizing structural management by quantifying the effect of land use and land cover on watershed
runoff and sediment yield. Water Resources Management, 21(11): 1899-1913.
Kanth, K. A., and Z. U. Hassan. 2010. Prioritization of watersheds in Wular catchment for sustainable development and management of natural
resources. Recent Research in Science and Technology, 2(4): 12-16.
Pandey, A., V. M. Chowdary, B. C. Mal, and M. Billib. 2009. Application of the WEPP model for prioritization and evaluation of best
management practices in an Indian watershed. Hydrological Processes, 23(21): 2997-3005.
Sarma, S., and T. Saikia. 2011. Prioritization of Sub-watersheds in Khanapara–Bornihat area of Assam–Meghalaya (India) based on land use and
slope analysis using remote sensing and GIS. Journal of the Indian Society of Remote Sensing, 40(3): 435-446.
Srivastava, R. K., H. C. Sharma, and A. K. Raina. 2010. Suitability of soil and water conservation measures for watershed management using
geographical information system. Journal of Soil and Water Conservation, 9(3): 148-153.
Ratnam, N. K., Y. K. Srivastava, V. V. Rao, E. Amminedu, and K.S. R. Murthy. 2005. Check dam positioning by prioritization micro-watersheds
using SYI model and morphometric analysis – remote sensing and GIS perspective. Journal of the Indian Society of Remote Sensing, 33(1):
25-38.
Thakkar, A. K., and S. D. Dhiman. 2007. Morphometric analysis and prioritization of mini-watersheds in a Mohr watershed, Gujarat using
remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing, 35(4): 313-321.
Singh, N. 1994. Remote sensing in the evaluation of morphohydrological characteristics of the drainage basin of Jojri catchment. Annals of Arid
Zone, 33(4): 273-278.
Vittala, S., Govindaiah, S. and Honne, G. H. 2004. Morphometric analysis of sub-watersheds in the Pavagada area of Tumkur district, South
India using remote sensing and GIS techniques Jour. Indian Soc. Remote Sensing (32), 4: 351-362.
Schumn, S. A. 1956. Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Geol. Soc. Am. Bull. 67: 597-646.
Oguchi, T. 1997. Drainage density and relative relief in humid steep mountains with frequent slope failure. Earth Surf. Processes Landforms,
22:107–120.