Mamasa River Basin is situated in South Sulawesi, Indonesia. In the recent years, the function of this basin can not be performed optimally in maintaining sustainability hydrologic function of Garugu dam. It is indicated by the occurrence of floods in rainy season and in contrary water shortage in dry season. Unexpected hydrology functions can be attributed to inappropriate land use at the upstream. Therefore, in order to maintain the hydrologic function of the dam, it is necessary to formulate suitable landuse at upstream of the river basin. This research is objected to formulate the suitable landuse, in term of reducing sedimentation rate in Garugu dam. This study employed RUSLE (Revised Universal Soil Loss Equation) for calculating erosion as well as sedimentation rate. Fuzzy Multi Attribute Decision Making (FMADM) and Analytic Hierarchy Process (AHP) which is combined with Geographical Information System (GIS), that ware used to formulate optimum landuse at the upstream. Focus Group Discussion (FGD) was conducted in order to validate the obtained landuse model using FMADM formulation. The optimum landuse is: composition for Mamasa River Basin that control of dead storage of sediment (DSS) is: 38.01% of agroforestry, 17.88% of forest, 15.66% of mixed agriculture, 11.30% of mixed garden and forest fruit crops, and the rest of the conservation actions and landuse such as strip cropping, rotation of crops, use bench terrace in rice fields, reforestation, cover crops and coffee plantation. The optimal landuse composition can control the sedimentation rate up to 127.22 (m3/km2)/yea

Farida, Sitti Nur

Hasanuddin University Repository

 9th International Conference on Hydroinformatics 
HIC 2010, Tianjin, CHINA 
 
 2301 
FUZZY MULTI ATTRIBUTE DECISION MAKING FOR RIVER 
BASIN MANAGEMENT 
 
AHMAD MUNIR, SITI NUR FARIDAH 
 Department of Agricultural Technology, University  
Hasanuddin, Jl P. Kemerdekaan 
km 11 Makassar, 90245, Indonesia 
 
Mamasa River Basin is situated in South Sulawesi, Indonesia. In the recent years, the 
function of this basin can not be performed optimally in maintaining sustainability 
hydrologic function of Garugu dam. It is indicated by the occurrence of floods in rainy 
season and in contrary water shortage in dry season. Unexpected hydrology functions can 
be attributed to inappropriate land use at the upstream. Therefore, in order to maintain the 
hydrologic function of the dam, it is necessary to formulate suitable landuse at upstream 
of the river basin.  This research is objected to formulate the suitable landuse, in term of 
reducing sedimentation rate in Garugu dam. This study employed RUSLE (Revised 
Universal Soil Loss Equation) for calculating erosion as well as sedimentation rate. 
Fuzzy Multi Attribute Decision Making (FMADM) and Analytic Hierarchy Process 
(AHP) which is combined with Geographical Information System (GIS), that ware used 
to formulate optimum landuse at the upstream. Focus Group Discussion (FGD) was 
conducted in order to validate the obtained landuse model using FMADM formulation. 
The optimum landuse is: composition for Mamasa River Basin that control of dead 
storage of sediment (DSS) is: 38.01% of agroforestry, 17.88% of forest, 15.66% of mixed 
agriculture, 11.30% of mixed garden and forest fruit crops, and the rest of the 
conservation actions and landuse such as strip cropping, rotation of crops, use bench 
terrace in rice fields, reforestation, cover crops and coffee plantation. The optimal landuse 
composition can control the sedimentation rate up to 127.22 (m3/km2)/year. 
 
INTRODUCTION 
 
Multiple Criteria Decision Making (MCDM) was introduced as a promising and 
important field of study in the early 1970’s. Since then the number of contributions to 
theories and models, which could be used as a basis for more systematic and rational 
decision making with multiple criteria, has continued to grow at a steady rate. One of the 
methods employed in MCDM is fuzzy sets. The theory of fuzzy sets proposed by Zadeh 
(Maedah and Murakami [4]), in 1965 has attracted wide spread attentions in various 
fields, especially where conventional mathematical techniques are of limited 
effectiveness, including landuse management and more.  
 This research is objected to formulate the suitable landuse, in term of reducing 
sedimentation rate in Garugu dam. The weighting factors for fuzzy process were 
determined by using the Analytic Hierarchy Process (AHP). Fuzzy process was applied 
  
2302 
for determining suitable landuse at the river basin. Sedimentation rate was determined by 
using RUSLE (Revised Universal Soil Loss Equation) method. Geographic Information 
System was employed as a tool for providing input data for erosion and sedimentation 
simulation.  
 
MODEL DEVELOPMENT 
 
The model is composed of geospatial model, fuzzy set, and decision criteria. Input data 
for running this study is: soil properties (structure, textures, permeability and organic 
content), landuse, vegetation and social and economic condition and secondary data 
covers satellite image of landuse and digital image of slope, ground cover, soil type, 
rainfall and conservation data. The data were obtained from PT. PLN and Burou for 
River Basin   Rehabilitation and Conservation Services South Sulawesi.     
All  analog data were converted into digital form in GIS format, the data analysis 
employs mainly GIS operator such as overlay, subtract etc. A GIS software named 
ArcView was used for building geospatial model. Input data for geospatial model is: 
rainfall erosivity (R), soil erodibility (K), Length-slope factors (LS), crop management (C) 
and conservation factors (P). Sedimentation simulation was generated by prediction 
erosion rate using RUSLE model. 
 
GIS model for Geospatial Model 
Input data for erosion model is realized using an interactive computer software ArcView. 
The software is applied for simulating geospatial of each input data. The thematic maps 
consist of erodibility, erosivity, and topographic, crop and conservation practice factors. 
The spatial pattern of erosion is calculated using RUSLE (Wischmeier and Smith [8]) as 
E = R*K*LS*C*P. The erosivity factor (R) was calculated using the equation developed 
by Bols [2]. The result of the erosivity calculations is processed in the geospatial model 
that is programmed in ArcView. The soil erodibility factor (K) is computed following the 
nomograph prepared by Wischmeier and Smith [8]. The topographic factor is computed 
following the formula suggested by Williams and Berndt [7].   
The landuse map is used to determine the C-factor values for each sub-unit land.                       
following the table provided by the Department of Agriculture, South Sulawesi (1999) 
(Munir et al. [6]). The C-factor is estimated based on the predominant landuse. The C-
factor is highest for bare land (1.0), and lowest for land that is fully covered with straw 
mulch (0.005). The P-factor accounts for onsite practices that reduce the effects of 
topography, slope length and slope angle, such as strip-cropping, contouring and 
terracing. The P value for each surface unit of land containing various conservation 
treatments can be estimated using the formula of Williams and Berndt [7]. 
 
Fuzzy Multi Attribute Decision Making (FMADM) 
Fuzzy Multi Attribute Decision Making (FMADM) was applied in this study in order 
to formulate optimum landuse. FMADM consists of: 1) building matrix of pairweise 
  
2303 
comparison (w); 2) weighting factors determination; 3) determination of    jwij xC  4) 
determination of    jwij xC interaction and 4) determination of optimum alternatives. 
Description of each steps as follows: 1) Determination of weighting factors (w) of 
each alternatives on each identified landuse (agroforestry,  forest, mixed agriculture, 
mixed garden and forest fruit crops, and  conservation actions and landuse such as 
strip cropping, rotation of crops, use bench terrace in rice fields, reforestation, cover 
crops and coffee plantation). With 
j
i
w
w  is important attributes on each criteria iw  and 
criteria jw . Assessment criteria of each identified landuse consist of environmental, 
economical and social benefits. The Membership function of each criteria is 
described as:     
~
,min 1,..., ; 1,...,j
w
i Cj iD x X i n j m  
. The fuzzy number of each 
weighting factor is figured  follows fuzzy graph; 2) Setting the weighting factor 
jW   
and obtaining consistency index by using eigenvector method as described by Saaty; 
3)  Calculate the value of    jwij xC ; 4) Determine of interaction of    j
w
ij xC , using 
the following formula:     ,min 1, , ; 1, ,jwi j cj iD x x i n j m    where Xi,   is 
computed using the following procedures; 5) Setting the ix , using the highest 
discordance index of D, and determine the optimum alternatives using the following 
the scheme described by Saaty; 6) Evaluation of Dead storage sediment (DSS) was 
conducted inorder to set the optimum quantity of sediment flow into dam. In this 
study, the value od DSS for Garugu dam is 132.57 m3/km2/yr. This value can 
maintain 50 years operation periods of the dam; 7) Validation. This steps was carried 
out in form of Focus Group Discussion (FGD). The obtained landuse from the 
FMADM model is compared with the FGD. In this study, the participant of FGD 
consists of the community of each represented landuse as described above. 
 
RESULT AND DISCUSSION 
 
Estimation of Sedimentation 
Erosion rates were computed using the RUSLE method (Wischmeier and Smith [8]). 
Sedimentation due to surface erosion (SEP) was computed using SEP = E · SDR, 
where E is erosion rate (t ha
-1
 year
-1
) and SDR is sediment delivery ratio computed 
using the formula described by Roehl (1962) 20.036  ASDR (Munir et al. [5]), where 
A is drainage area (km2). Sedimentation rate (m3 km-2 year-1) was computed using a 
density of the soil of 1200 kg·m-3. The value of SDR for Mamasa River Basin with 
the total area of 106178.03 ha is 8.97% or 0.09. Sedimentation rate of each existing 
landuse is:  
 
 
 
  
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Table 1. Sedimentation Rate of each existing landuse 
 
No. Penggunaan Lahan Area(ha) Sediment (m3/km2/yr) 
1 Forest 54126.20 59.51 
2 Paddy Field 2261.72 128.63 
3 underbush 37254.55 2767.90 
4 Mixed Agriculture 2684.70 2627.10 
5 Dry land agriculture 9850.86 2177.82 
Total 106178,03 7768.98 
 
Contribution of sediment from the existing landuse on Bakaru dam is 7768.98 
m3/km2/yr. According to Lin et al. [3], the susspended sediment that have high bulk 
density can settled on water body and in a certain condition, it will increse operation cost 
of hydrologic infrastructure. This situation is occured in Bakaru dam. 
 
Landuse Decision Using FMADM 
Fuzzy Multi Attribute Decision Making (FMADM) was applied for deciding optimum 
land use in Mamasa River Basin (Zhen et al. [9]), this method is rarely applied for 
erosion management. However this method has a numerous potential for application in 
soil water conservation. The first step of this study is determination of weighting factors 
(w) of each alternatives on each identified landuse (agroforestry, forest, mixed 
agriculture, mixed garden and forest fruit crops, and  conservation actions and landuse 
such as strip cropping, rotation of crops, use bench terrace in rice fields, reforestation, 
cover crops and coffee plantation). The matrix composition w for each identified landuse 
was determined using AHP method.  
 
Tabel 2. Contribution of sediment of landuse development using FMADM 
 
No. Development Scenario of FMADM erosion 
(ton/ha/yr) 
sedimen 
(m3/km2/yr) 
1 - 718.61 7760.98 
2 scenario 1 189.04 2041.43 
3 scenario 2 52.98 573.81 
4 scenario 3 20.29 219.13 
5 scenario 4 11.78 127.22 
 
Landuse Evaluation 
Landuse development using FMADM (development scenario 1) can decrease 
sedimentation rate to 73.69%, however this value can not reach DSS of the dam. 
Therefore scenario 1 can not be recommended for optimizing landuse on the river basin. 
Base on these, landuse scenario must be further developed. Sedimentation rate on 
scenario 4 is 127.22 m3/km2/yr. This value can achieve below DSS of the dam. This value 
  
2305 
must be maintain for sustainability function of the dam for 50 years operation periods. 
Scenario 4 is  recommende landuse for the river basin as shown in the following figure.   
From FMADM, 38.01% of the total coverage of the basin are occupied by the 
agroforestry. This landuse type dominated the coverage of the basin. According to   
Angima et al. [1], benefits of agroforestry is not only for soil conservation but also for 
generating additional income for the community.  
Mixed agriculture is  the second level of landuse preferency of the community of the 
basin.  Certain portion of the coverage must be reforested, especially at critical area as 
shown on the map. 7839.66 ha request reforestation.  Coffee plantation is the most 
common plantation on the basin. This type of the plantation occupied 3.80% of the 
coverage. 
Conservation practice is requested also for maintaining sustainability of hydrologic 
function of the basin. The most common conservation practice in the basin is strip 
cropping, crop rotation and cover crops. These type of conservation practices occupied 
the basin 2.92 %, 1.59 % and 0.08% respectively. The recommended landuse is presented 
in Table 3. 
 
Figure 1. Recommended landuse for the Mamasa River Basin 
 
  
2306 
Tabel 3. Recommended landuse composition using FMADM  for Mamasa River Basin   
 
No. Landuse Area (ha) Persentage (%) 
1 Forest 18994.10 17.88 
2 Agroforestri 39525.63 38.01 
3 Reforestration 7839.66 7.37 
4 Agroforestry (fruit crops) 12009.14 11.30 
5 Mixed agriculture 16633.80 15.66 
6 Coffee plantation 4028.34 3.80 
7 Bench terrace 2261.72 2.13 
8 Strip croping 3101.40 2.92 
9 Crop rotation 1697.42 1.59 
10 Cover crops 86.83 0.08 
Totol 106178.03 100 
 
 
Validation of Landuse generated from FMADM 
Validation of generated landuse was carried out by conducting focus group discussion 
(FGD). This action is a qualitative study to compare between the landuse formulation 
obtained from FMADM simulation  and the preferency of the community on the Mamasa 
River Basin. The parcipants of the FGD consists of the represented community of each 
landuse type (forestry, paddy field, mixed agriculture, underbush). The result of the FGD 
is presented in the following table. 
 
Table 4. Results of FGD on each representative landuse of Mamasa River Basin  
 
No. Landuse Result of FGD 
1 Paddy field 
The communities represented by paddy field prefer to 
introduce bench terrace. The main reason is bench terrace 
is not only can reduce overland flow but also contributes 
to increase production rate of paddy field 
2 Underbush 
The community expect to carried out land clearing of 
the coverage occupied by underbush.Therefore underbush 
area can be explore to be productive land 
3 Mixed agriculture 
Numerous owners of  mixed agricultural land prefer to 
change their land become agroforestry. Alley cropping is 
an alternative in mixed agriculture 
4 Dryland agriculture 
Strip cropping is the common preference of the 
represented community from dry land agriculture. The 
reason is strip cropping contributes not only for 
increasing benefit but also in soil conservation 
 
Generally from the FGD, it was obtained that in managing their land, they consider 
not only economic orientation but also environmental point of view. They expect to make 
  
2307 
balance between economy and environment consideration. Mostly, the community 
suggest to implement agroforestry in farming practices. This is relevant with the results 
that were generated from FMADM model (the most suitable of conservation practices is 
agroforestry). Bench terrace is the most recommended conservation practices for paddy 
field. According to the farmer this conservation practices contributes to increment of 
biomass production. Table 5 presents landuse obtained from FGD and  landuse obtained 
from FMADM. 
 
Table  5. Landuse obtained from FGD and FMADM 
 
No. Existing landuse 
Landuse obtained from 
FMADM 
Area (ha) 
1 Forest 
Forest 18994.10 
Agroforestry 35132.10 
2 Paddy Field Benc terrace 2261.72 
3 Underbush 
Mixed agriculture 16633.80 
Reforestration 7839.66 
Forest with fruit crops 8752.75 
Coffee 4028.34 
4 
Mixed agriculture  
 
Strip Croping 37.41 
Forest with fruit crops 1054.74 
Reforestration 0.14 
Agroforestry 1592.42 
5 Dry land agriculture 
Forest with fruit crops 2201.65 
Cover crops 86.83 
Agroforestry 2801.11 
Strip Croping 3063.99 
Crop rotation 1697.28 
Total 106178.03 
Furthermore, from FMADM, it was obtained that mixed agriculture with strip 
cropping is recomended for conservation practices. This is relevant with the information 
obtained from FGD that was conducted in this area. 
 
CONCLUSIONS 
 
Application of FMADM in landuse management at Mamasa River Basin can be 
effectively applied to reduce sedimentation rate to 98.36 % which is equal to 127.22 
m3/km2/yr. This level is below dead storage of sediment of Garugu Dam.  
The generated sedimentation rate is contributed by the following landuse 
composition: 38.01 % of agroforestry, 17.88 % of forest, 15.66 % of mixed agriculture, 
11.30 % of forest with fruit crops and the rest Strip Cropping, crop rotation, bench terrace 
at paddy field, reforestration, cover crops and coffee plantation. 
  
  
2308 
ACKNOWLEDGEMENTS 
 
The Author wish to thank to the Ministry of Research and Technology of the Republic of 
Indonesia for supporting this research under the project of Riset Insentif in 2008.  
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FUZZY MULTI ATTRIBUTE DECISION MAKING FOR RIVER BASIN MANAGEMENT

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FUZZY MULTI ATTRIBUTE DECISION MAKING FOR RIVER BASIN MANAGEMENT

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