An application of the AHP in water resources management: a case study on urban drainage rehabilitation in Medan City

IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS An application of the AHP in water resources management: a case study on urban drainage rehabilitation in Medan City To cite this article: A P M Tarigan et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 309 012096 View the article online for updates and enhancements. This content was downloaded from IP address 148.251.232.83 on 10/10/2018 at 03:51

An application of the AHP in water resources management: a case study on urban drainage rehabilitation in Medan City A P M Tarigan 1*, D Rahmad 2, R A Sembiring 3, and R Iskandar 4. 1,4 Teaching Staff, Civil Engineering Postgraduate Study Programs, Universitas Sumatera Utara 2 Master s Student, Civil Engineering Postgraduate Study Programs, Universitas Sumatera Utara 3 Teaching Staff, Civil Engineering Department, Universitas Sumatera Utara *E-mail: a.perwira.mulia@gmail.com Abstract. This paper illustrates an application of Analytical Hierarchy Process (AHP) as a potential decision-making method in water resource management related to drainage rehabilitation. The prioritization problem of urban drainage rehabilitation in Medan City due to limited budget is used as a study case. A hierarchical structure is formed for the prioritization criteria and the alternative drainages to be rehabilitated. Based on the AHP, the prioritization criteria are ranked and a descending-order list of drainage is made in order to select the most favorable drainages to have rehabilitation. A sensitivity analysis is then conducted to check the consistency of the final decisions in case of minor changes in judgements. The results of AHP computed manually are compared with that using the software Expert Choice. It is observed that the top three ranked drainages are consistent, and both results of the AHP methods, calculated manually and performed using Expert Choice, are in agreement. It is hoped that the application of the AHP will help the decision-making process by the city government in the problem of urban drainage rehabilitation. 1. Introduction 1.1 Background and objective With the growth of population and urbanization, a large and densely populated city in a developing country like Medan City will experience lots of land changes that cause a significant increase in runoff coefficients. As the run off coefficients become higher, the ability of the land surfaces to recharge water to the ground water aquifer become less effective, increasing the stream flows during and after significant rainfall events. In other words, because of the urbanized areas with more paved streets and parking lots, buildings and roof tops which prevent seepage and increase runoff, more water from the rainfall enters the city drainage. On the other hand, the city drainages suffer from substantial sedimentation resulting in the reduction of the effective cross-section of the drainages. Consequently, the capacity of the drainages to flow water becomes reduced. As a result of the condition in which the run-off increases while the drainage capacity decreases, flood is inevitable especially after heavy rain. Stagnant water cannot be addressed because the drainage is not working properly drain water from the road. Roads drainage only designed and built without regard to good or bad performance. Many found that the roads drainage has been constructed does not work properly (not able to drain the Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by Ltd 1

stagnant water in the road because of the condition of the existing drainage damaged, clogged, not well maintained, high sediment and others) [1]. In order to mitigate the flood hazards, the city drainages need to be improved or rehabilitated regularly. However, the city budget is not sufficient to do regular maintenance works for such extensive drainages within the whole city area. In this case prioritization is required, especially over the hot spots where flood events frequently occur. In order to reach these goals, we need to design a system with systematic processes [2]. And The purpose of this study is to illustrate the application of Analytical Hierarchy Process (AHP) as a potential decision-making method in water resource management related to drainage rehabilitation. The prioritization problem of urban drainage rehabilitation over selected hot spots in a district in Medan City is used as a study case. It is hoped that the application of the AHP will help the decisionmaking process by the city government in the problem of urban drainage rehabilitation. 1.2 Analytical Hierarchy Process (AHP) The AHP, developed by Saaty, is one of the methods based on the concept of the multi criteria decision analysis [3-6]. The concept is used to help decision makers learn about their problems in a decision problem situation in which discretely defined alternatives are to be evaluated. More than one criterion (or attribute) are involved in the decision process to reach the preferred alternatives in a ranked fashion. Based on the judgement of the decision maker, the AHP aims at quantifying relative priorities for a given set of alternatives on a ratio scale and stresses the importance of the consistency of the comparisons of alternatives [7]. The procedures of the AHP can be outlined as follows. Step1: Define the problem and its objective. Step 2: Set up the decision hierarchy. Step 3: Make pairwise comparisons of attributes and alternatives using the relative scale measurement shown in Table 1. Step 4: Transform the comparison into weights and check the consistency of the decision maker s comparison using Table 2. Step 5: Use the weights to obtain scores for the different options and make a provisional decision. And Step 6: Perform sensitivity analysis. Table 1. Pairwise comparison scale for AHP preferences [3-6] Numerical rating Verbal judgments of preferences 9 Extremely preferred 8 Very strongly to extremely 7 Very strongly preferred 6 Strongly to very strongly 5 Strongly preferred 4 Moderately to strongly 3 Moderately preferred 2 Equally to moderately 1 Equally preferred Table 2. Average random consistency (Ri)[3-6] Ordo Matrix 1 2 3 4 5 6 7 8 9 10 RI 0 0 0,58 0,9 1,12 1,24 1,32 1,41 1,45 1,49 2. Method The locations of the study, consisting of 7 sewer drainages, are located within the district of Medan Baru which is one of the total 21 districts in Medan City. University of Sumatera Utara (USU) is 2

located in Medan Baru with elevation approximately 30 m above sea level and distance 30 km from the coastline [8]. Note that we observe frequent flooding at USU and its vicinities during heavy rain. The locations are selected due to the frequent flooding observed in their vicinities. The main data in this study are obtained from interview and questionnaire which is designed for the AHP method. The AHP allows group decision making, where group members can use their experience, values and knowledge to break down a problem into a hierarchy and solve it by the AHP steps [9]. There were nine respondents who were interviewed and did fill in the questionnaire. All of them are considered knowledgeable about the flood problems occurring in the study locations. There are two stages of interview conducted in this study. Stage 1 is the cut off method which aims at defining the criteria and sub-criteria contained within the AHP. Stage 2 is the full AHP interview with questionnaire in which the whole pairwise comparisons are given. The values taken into consideration for the cut off method as well as the AHP are the arithmetic averaged values given by all respondents. 3. Results and discussions 3.1 The cut off method As mentioned above, the purpose of the cut off method is to define the criteria and sub-criteria of the AHP based on the defined objective [10, 11], i.e. to rank the prioritization of the drainage rehabilitation. The questionnaire used was given in 5 values of classification from 0 to 4 as follows: 0 for not important, 1 for less important, 2 for important, 3 for more important, and 4 very important. Based on the questionnaire answered by the respondents, from 19 sub-criteria included in 4 criteria (or aspects), we selected 8 sub-criteria to be involved in the next stage of interview. The selection process was done based on a threshold value of the cut off and the principle of the absence of redundancy. Note that if two criteria duplicate each other because they may represent the same thing, then one of these criteria can be said redundant and be neglected. Figure 1 shows the final hierarchy of the drainage rehabilitation comprising of the criteria and sub-criteria used in the AHP method. Level 1: Goal of drainage rehabilitation Level 2: Criteria TA SA CA Level 3: Subcriteria DDL DS IC FAP TD EAD PP CC Level 4: Alternatives D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 TA = Technical Aspects CI = Inundation Condition CC = Community Character SA = Social Economic Aspects FAP = Flood Affected Population CA = Cultural Aspects TD = Traffic Disturbance DDL = Drainage Damage Level EAD = Economic Activities Disturbance DS = Drainage Status PP = Public Participation D1, D2, D3, D4, D5, D6, D7, D8, D9 and D10 are the alternatives. Figure 1. The final hierarchy of the drainage rehabilitation 3.2 The AHP process First, we process the pairwise comparison for the criteria (level 2 in the hierarchy), i.e. technical aspects, social aspects, and cultural aspects. The result is shown in Table 3. 3

Table 3. Pairwise comparison matrix for the criteria (level 2) Technical aspects Social aspects Culture aspects Technical aspects 1 0.42 2.4 Social aspects 2.4 1 2.8 Culture aspects 0.42 0.36 1 = 3.82 1.77 6.20 Dividing each the element of the above matrix by its respective column total results in the following. 0.262 0.629 0.109 0.235 0.564 0.201 0.387 0.452 0.161 The priority which indicates the weight for each aspect is the row average as follows: 0.295 0.548 0.157 The priority is then multiplied with the elements of the initial matrix in Table 1 to get the following. 0.295 1 2.4 0.42 + 0.548 0.42 1 0.36 + 0.157 2.4 2.8 1 = 0.900 1.696 0.476 The above is divided by the respective priority to yield:... = 3.056, = 3.094,... = 3.025 Then the maximum principal eigen value λ max is obtained by averaging the above values: (...) λ max = = 3.058 The consistency index Ci is computed as follows λ!" Ci = #!$ = %.&'(!% %!$ = 0.029 With the random index Ri = 0.58 (Table 2) for size of matrix n = 3, the consistency ratio Cr can be calculated as follows: Cr = )* =. +*, = 0.050 Cr = 0.050 < 0.1, OK Since Cr less than 0.1, we can conclude that the comparisons are consistent and thus acceptable. The same procedures are conducted for the comparisons of the sub-criteria (level 3 in the hierarchy) as well as for the alternative drainages (level 4 in the hierarchy). Tables 4, 5, and 6 show the results of the comparisons of the sub-criteria, i.e. technical aspects, social aspects, and culture aspects, consecutively. 4

Table 4. Pairwise comparison matrix for Technical aspects Drainage status Level of drainage Condition of inundation damage Drainage status 1 0.769 0.625 0.252 Level of drainage 1.3 1 0.5 0.278 damage Condition of inundation 1.6 2 1 0.470 λ maks = 3.026, Ci = 0.013, Ri = 0.58, Cr = 0.002 < 0.1, OK Table 5. Pairwise comparison matrix for social aspects Flood affected population Traffic disturbance Economic activities disturbance Flood affected 1 2.4 2.4 0.529 population Traffic 0.42 1 2.4 0.301 disturbance Economic activities disturbance 0.42 0.42 1 0.170 λ max = 3.018, Ci = 0.004, Ri = 0.58, Cr = 0.0158 < 0.1, OK Table 6. Pairwise comparison matrix for culture aspects Public participation Community characters Public participation 1 2.4 0.706 Community characters 0.417 1 0.294 λ max = 2, Ci = 0, Ri =0.00, Cr = 0 < 0.1, OK There are 10 alternative drainages to be compared. They are denoted as follows: D1 = Sei Putih drainage, D2 = Sei Asahan 1 drainage, D3 = dr. Mansyur drainage, D4= Mandolin&Rebab drainage, D5 = Sei Selayang drainage, D6 = Sei Mencirim drainage, D7 = Darussalam drainage, D8 = Djamin Ginting drainage, D9= Sei Padang drainage, and D10 = Sei Asahan 2 drainage. The pairwise comparison matrices of all alternative drainages for all sub-criteria are given in Tables 7 to 14. Since there are 8 sub-criteria, then we have the following 8 sub-criteria matrices. Table 7. Pairwise comparison matrix for level of drainage damage DDL D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D1 1.00 0.33 0.42 0.42 0.42 0.42 0.29 2.40 2.40 0.25 0.055 D2 3.00 1.00 0.42 0.42 2.40 2.40 0.42 2.40 2.40 0.42 0.097 D3 2.40 2.40 1.00 3.50 3.50 2.40 2.40 2.40 4.00 2.40 0.213 D4 2.40 2.40 0.29 1.00 2.50 2.40 0.33 2.40 2.40 0.42 0.106 D5 2.40 0.42 0.29 0.40 1.00 2.40 0.29 2.40 0.42 0.42 0.067 D6 2.40 0.42 0.42 0.42 0.42 1.00 0.42 2.40 2.40 1.00 0.077 D7 3.40 2.40 0.42 2.40 3.50 2.40 1.00 3.50 3.00 1.00 0.156 D8 0.42 0.42 0.42 0.42 0.42 0.42 0.29 1.00 0.42 0.50 0.040 D9 0.42 0.42 0.25 0.42 2.40 0.42 0.33 2.40 1.00 0.50 0.056 D10 4.00 2.40 0.33 2.40 2.40 1.00 1.00 2.00 2.00 1.00 0.131 = 1.00 λmax = 11.113, Ci = 0.0126, Ri =1.49, Cr = 0.0845 < 0.1, OK 5

Table 8. Pairwise comparison matrix for drainage status DS D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D1 1.00 0.42 0.42 1.00 0.42 1.00 1.00 0.42 0.42 1.00 0.059 D2 2.40 1.00 1.00 2.40 1.00 2.40 2.40 1.00 1.00 2.40 0.141 D3 2.40 1.00 1.00 2.40 1.00 2.40 2.40 1.00 1.00 2.40 0.141 D4 1.00 0.42 0.42 1.00 0.42 1.00 1.00 0.42 0.42 1.00 0.059 D5 2.40 1.00 1.00 2.40 1.00 2.40 2.40 1.00 1.00 2.40 0.141 D6 1.00 0.42 0.42 1.00 0.42 1.00 1.00 1.00 0.42 1.00 0.067 D7 1.00 0.42 0.42 1.00 0.42 1.00 1.00 0.42 1.00 1.00 0.065 D8 2.40 1.00 1.00 2.40 1.00 1.00 2.40 1.00 2.40 2.40 0.147 D9 2.40 1.00 1.00 2.40 1.00 2.40 1.00 0.42 1.00 2.40 0.123 D10 1.00 0.42 0.42 1.00 0.42 1.00 1.00 0.42 0.42 1.00 0.059 λmax = 10.195, Ci = 0.0216, Ri =1.49, Cr = 0.0145 < 0.1, OK Table 9. Pairwise comparison matrix for condition of inundation IC D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D1 1.00 0.42 0.22 2.40 2.40 2.40 0.33 2.40 2.40 0.33 0.089 D2 2.40 1.00 0.25 2.40 2.40 1.40 0.33 2.40 2.00 0.42 0.096 D3 4.50 4.00 1.00 3.00 4.50 2.40 1.00 2.40 2.40 3.00 0.212 D4 0.42 0.42 0.33 1.00 2.40 2.40 0.42 2.40 2.40 0.42 0.081 D5 0.42 0.42 0.22 0.42 1.00 2.40 0.33 3.00 2.40 0.50 0.069 D6 0.42 0.71 0.42 0.42 0.42 1.00 0.42 2.40 2.00 0.50 0.061 D7 3.00 3.00 1.00 2.40 3.00 2.40 1.00 3.00 2.40 2.00 0.175 D8 0.42 0.42 0.42 0.42 0.33 0.42 0.42 1.00 0.50 0.33 0.040 D9 0.42 0.50 0.42 0.42 0.42 0.50 0.42 2.00 1.00 0.42 0.049 D10 3.00 2.40 0.33 2.40 2.00 2.00 0.50 3.00 2.40 1.00 0.127 λmax = 11.079, Ci = 0.120, Ri = 1.49, Cr = 0.0805 < 0.1, OK Table 10. Pairwise comparison matrix for flood affected population FAP D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D1 1.00 2.40 0.42 2.40 2.00 1.00 2.40 3.00 2.40 0.33 0.128 D2 0.42 1.00 0.42 0.50 0.50 0.33 0.42 1.40 0.42 0.33 0.045 D3 2.40 2.40 1.00 2.40 2.40 2.00 2.40 2.40 2.40 2.40 0.189 D4 0.42 2.00 0.42 1.00 1.40 0.42 0.42 2.40 1.00 1.00 0.076 D5 0.50 2.00 0.42 0.71 1.00 0.42 0.42 2.40 1.00 1.00 0.072 D6 1.00 3.00 0.50 2.40 2.40 1.00 2.40 2.40 3.00 3.00 0.162 D7 0.42 2.40 0.42 2.40 2.40 0.42 1.00 2.40 0.42 0.42 0.089 D8 0.33 0.71 0.42 0.42 0.42 0.42 0.42 1.00 0.42 0.42 0.042 D9 0.42 2.40 0.42 1.00 1.00 0.33 2.40 2.40 1.00 1.00 0.088 D10 2.40 2.40 0.42 1.00 1.00 0.33 2.40 2.40 1.00 1.00 0.109 λmax = 10.743, Ci = 0.826, Ri = 1.49, Cr = 0.0554 < 0.1, OK Table 11. Pairwise comparison matrix for traffic disturbance = 1.00 TD D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D1 1.00 1.40 0.33 1.40 1.40 0.42 0.33 2.40 2.40 2.00 0.093 D2 0.71 1.00 0.29 2.00 2.00 1.00 0.42 2.40 2.00 2.00 0.099 D3 3.00 3.50 1.00 3.00 2.40 3.00 2.40 2.40 2.40 2.40 0.214 D4 0.71 0.50 0.33 1.00 1.00 0.42 0.33 1.40 1.70 2.40 0.071 D5 0.71 0.50 0.42 1.00 1.00 1.00 0.50 2.40 2.00 2.40 0.087 D6 2.40 1.00 0.33 2.40 1.00 1.00 0.42 2.40 2.40 3.00 0.116 D7 3.00 2.40 0.42 3.00 2.00 2.40 1.00 2.00 2.00 0.71 0.150 D8 0.42 0.42 0.42 0.71 0.42 0.42 0.50 1.00 0.42 0.59 0.045 D9 0.42 0.50 0.42 0.59 0.50 0.42 0.50 2.40 1.00 0.50 0.055 D10 0.50 0.50 0.42 0.42 0.42 0.33 1.40 1.70 2.00 1.00 0.070 λmax = 10.886, Ci = 0.098, Ri = 1.49, Cr = 0.0661 < 0.1, OK 6

Table 12. Pairwise comparison matrix for economic activities disturbance EAD D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 Vector D1 1.00 0.71 0.33 1.00 0.42 0.33 0.42 1.00 1.00 1.00 0.057 D2 1.40 1.00 0.42 1.00 0.50 0.42 0.42 1.00 0.42 1.00 0.060 D3 3.00 2.40 1.00 2.40 2.00 0.42 2.40 3.40 2.40 2.40 0.170 D4 1.00 1.00 0.42 1.00 0.50 0.42 0.42 1.00 0.50 1.00 0.058 D5 2.40 2.00 0.50 2.00 1.00 0.42 0.42 2.40 2.00 2.40 0.113 D6 3.00 2.40 2.40 2.40 2.40 1.00 2.40 2.40 2.00 3.00 0.202 D7 2.40 2.40 0.42 2.40 2.40 0.42 1.00 2.00 2.00 2.40 0.134 D8 1.00 1.00 0.29 1.00 0.42 0.42 0.50 1.00 0.42 1.00 0.056 D9 1.00 2.40 0.42 2.00 0.50 0.50 0.50 2.40 1.00 2.40 0.096 D10 1.00 1.00 0.42 1.00 0.42 0.33 0.42 1.00 0.42 1.00 0.055 λ max = 10.392, Ci = 0.0436, Ri = 1.49, Cr = 0.0292 < 0.1, OK Table 13. Pairwise comparison matrix for public participation PP D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D1 1.00 2.40 0.42 1.00 0.50 1.00 1.00 3.00 2.40 0.42 0.092 D2 0.42 1.00 0.42 0.42 0.33 0.42 0.42 2.00 2.40 0.42 0.058 D3 2.40 2.40 1.00 2.80 0.42 2.00 2.40 3.00 3.00 1.00 0.155 D4 1.00 2.40 0.36 1.00 0.50 1.00 1.00 2.00 2.40 0.42 0.086 D5 2.00 3.00 2.40 2.00 1.00 2.00 2.40 3.00 2.40 2.40 0.196 D6 1.00 2.40 0.50 1.00 0.50 1.00 1.00 2.40 2.40 0.42 0.090 D7 1.00 2.40 0.42 1.00 0.42 1.00 1.00 1.00 2.40 0.42 0.081 D8 0.33 0.50 0.33 0.50 0.33 0.42 1.00 1.00 1.20 0.33 0.047 D9 0.42 0.42 0.33 0.42 0.42 0.42 0.42 0.83 1.00 0.42 0.043 D10 2.40 2.40 1.00 2.40 0.42 2.40 2.40 3.00 2.40 1.00 0.152 λ max = 11.463, Ci = 0.0515, Ri = 1.49, Cr = 0.0345 < 0.1, OK Table 14. Pairwise comparison matrix for community characters CC D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D1 1.00 2.00 1.40 1.00 0.42 1.00 1.00 3.00 2.40 3.00 0.118 D2 0.50 1.00 1.00 0.50 0.33 0.42 0.42 3.00 2.40 2.40 0.080 D3 0.71 1.00 1.00 1.40 0.42 0.42 0.42 3.00 3.40 2.40 0.099 D4 1.00 2.00 0.71 1.00 0.42 1.00 1.00 2.00 2.00 2.40 0.104 D5 2.40 3.00 2.40 2.40 1.00 2.40 2.40 3.00 2.00 4.00 0.212 D6 1.00 2.40 2.40 1.00 0.42 1.00 1.00 2.00 2.40 3.50 0.127 D7 1.00 2.40 2.40 1.00 0.42 1.00 1.00 2.00 2.00 2.00 0.118 D8 0.33 0.33 0.33 0.50 0.33 0.50 0.50 1.00 0.50 2.00 0.049 D9 0.33 0.33 0.25 0.50 0.50 0.33 0.50 2.00 1.00 2.00 0.057 D10 0.33 0.42 0.42 0.42 0.25 0.29 0.50 0.50 0.50 1.00 0.038 λ max = 10.446, Ci = 0.0519, Ri = 1.49, Cr = 0.034 < 0.1, OK Having made all the matrices from Table 5 to 14, we can calculate the score of priority for each drainage. The overall priority score of drainage D1 can be computed and is given below as an example, whereas Table 15 shows the summary for computation of the scores for all alternative drainages. The overall priority score of drainage D1 = (0.295 X 0.252 X 0.55) + (0.295 X 0.278 X 0.059) + (0.295 X 0.470 X 0.089) + (0.548 X 0.529 X 0.128) + (0.548 X 0.301 X 0.093) + (0.548 X 0.301 X 0.177) + (0.157 X 0.706 X 0.092) + (0.157 X 0.262 X 0.118) = 0.094 7

Technical aspects (0.295) Table 15. Summary of score computation Social aspects (0.548) Culture aspects (0.157) DDL DS CI FAP TD EAD PP CC (0.252) (0,278) (0.470) (0.529) (0.301) (0.177) (0.706) (0.262) D1 0.055 0.059 0.089 0.128 0.093 0.057 0.092 0.118 0.094 D2 0.097 0.141 0.096 0.045 0.099 0.060 0.058 0.080 0.076 D3 0.213 0.141 0.212 0.189 0.214 0.170 0.155 0.099 0.184 D4 0.106 0.059 0.081 0.076 0.071 0.058 0.086 0.104 0.077 D5 0.067 0.141 0.069 0.072 0.087 0.113 0.196 0.212 0.102 D6 0.077 0.067 0.061 0.162 0.116 0.202 0.090 0.127 0.120 D7 0.156 0.065 0.175 0.089 0.150 0.134 0.081 0.118 0.119 D8 0.040 0.147 0.040 0.042 0.045 0.056 0.047 0.049 0.052 D9 0.056 0.123 0.049 0.088 0.055 0.096 0.043 0.057 0.071 D10 0.131 0.059 0.127 0.109 0.070 0.055 0.152 0.038 0.099 Overall priority Sensitivity analysis is made by neglecting the culture aspects. This is done with the consideration that the judgement for the culture aspects are quite difficult if compared for the other aspects. Also the culture aspects have the least weight (15.7%). Hence, we have only 2 criteria, i.e. technical and social aspects, in the second level of the hierarchy. With this change, we find that ranks 1, 2 and 3 are still consistently unchangeable, i.e. D3 = Dr. Mansur, D6 = Sei Mencirim, D7 = Darussalam, consecutively. In other words, the top three are among the most favorable to have rehabilitation. Also we do computation using Expert Choice. We find that the same results are obtained. 4. Concluding remarks We have shown the applicability the AHP as a potential decision-making method in water resource management related to the drainage rehabilitation. As a study case, we are able to rank 10 urban drainages in the Medan Baru district in Medan City for rehabilitation. Based on the sensitivity analysis made, it is observed that the top three ranked drainages are consistent. Also both results of the AHP methods, calculated manually and performed using Expert Choice, are in agreement. It is hoped that the application of the AHP will help the decision-making process by the city government in the problem of urban drainage rehabilitation. References [1] Kamil I, et al. 2013 Design of performance evaluation tools for drainage of roads system in developing country (case study: drainage system for city roads in padang indonesia). Proc. of the Int. Sym. on the AHP (Kuala lumpur, Malaysia) [2] Huang H.F, Chen H C and Liu S 2015 Management strategies for Taiwan shimen reservoir catchment area: perspectives of collaborative planning. Int. J. of the AHP. vol.7(1) 121-137 [3] Saaty T L 1980 The Analytic Hierarchy Process (New York: Mc Graw Hill) [4] Saaty T L 1985 Decision making for leaders (Belmont, California) [5] Saaty T L 1990 How to make a decision: the AHP. European j. of oper. research (North-holland) 48 9-26 [6] Saaty T L and Kearns K P 1991 Analytical planning: the organization of systems. The analytic hierarchy process series 1991 4RWS (Pittsburgh,USA) [7] Triantaphyllou E and Mann S H 1955 Using the Analytic Hierarchy Process for decision making in Engineering Applications: Some Challenge. Int. J. of Industrial Engineering: Applications and Practice, vol.2(1) pp. 35-44 8

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