Subscribe Now Subscribe Today
Fulltext PDF
Research Article
Prioritization of Factors Impacting on Water Security using Analytic Hierarchy Process Method

Tran Thi Lan Huong, Nguyen Truc Le, Do Anh Duc and Nguyen Xuan Long
Background and Objective: Water Source Security (WSS) is one of the key challenges for many countries due to climate change, limited water supply and growing water demand. The objective of this study was to find out the important factors which influence the security of water sources in the mainstream of the "Da" River, Vietnam. Materials and Methods: In this study, three groups of factors affecting WSS were considered including, (1) Policies and mechanisms factors, (2) Water demand factors and (3) Natural factors. Fuzzy analytic hierarchy process method was applied to quantify the factors which influence WSS. The input data were collected by means of face-to-face interviews with water resources experts. Results: The results show that the group of natural factors were the most affecting group to water security, followed by the group of mechanism and policy factors and group of water demand factors. Conclusion: This study has found out the most affecting group to WSS using the fuzzy AHP approach. The results obtained from this study may serve as a reference for policy makers in water resources management.
Related Articles in ASCI
Similar Articles in this Journal
Search in Google Scholar
View Citation
Report Citation

  How to cite this article:

Tran Thi Lan Huong, Nguyen Truc Le, Do Anh Duc and Nguyen Xuan Long, 2017. Prioritization of Factors Impacting on Water Security using Analytic Hierarchy Process Method. Asian Journal of Scientific Research, 10: 236-243.

DOI: 10.3923/ajsr.2017.236.243

Received: April 13, 2017; Accepted: May 26, 2017; Published: June 15, 2017


Water Source Security (WSS) poses one of the biggest challenges of the 21st century. Water security is defined as the availability of an acceptable quantity and quality of water for health, livelihoods, ecosystems and production, coupled with an acceptable level of water-related risks to people, environments and economies1. Due to changing climate, limited water supply and growing water demand, humanity faces the prospect of uncertain future water supplies2-4. It was estimated that the world would face a 40% shortfall of water supply in the next 15 years5. This shows that the assurance of water source is becoming an urgent and pressing issue for many countries.

Many studies have looked at different aspects of WSS1-4,6-13. However, a limitted studies has investigated the WSS in Vietnam, especially the studies on risks of water insecurity in the river basin14.

Now a days, Vietnam is facing a major challenge of WSS due to the uneven distribution of water sources, environmental pollution and declining water source in both quantity and quality and a strong reliance on external water sources. In addition, under the impact of industrialization, modernization, population pressure, urbanization, rising food demand, the shrinking of agricultural land and watershed forests are happening in a complex way, which has made the security of water supply in some places seriously threatened14. To overcome this situation, systematic and consistent measures must be taken throughout the river basin with a view of water resources management, water decrease management. Meanwhile, Vietnam does not have a legal tool with sanctions strong enough to protect and guarantee water supply security, sustainable development, environmental protection, ecosystems.

Fuzzy Analytic Hierarchy Process (AHP) is one of the most widely used tools for multiple criteria decision-making. Recent applications of fuzzy AHP methods are found15-24. Among the existing AHP approaches, the extent analysis method proposed by Chang25 is a commonly used approach that is highly cited and has wide applications.

The objective of this study was to apply the fuzzy AHP technique proposed by Chang25 to determine the important factors affecting WSS in Vietnam.


Fuzzy sets theory: This study reviews some basic notions and definitions of fuzzy sets and fuzzy numbers as follows26,27:

Definition 1: A real fuzzy number A is described as any fuzzy subset of the real line R with membership function fA that can be generally be defined as:

(a) fA is a continuous mapping from R to the closed interval [0, ], 0<<1
(b) fA (x) = 0, for all xε(-∞, a]
(c) fA is strictly increasing on[a, b]
(d) fA (x) = , for all x ε[b, c]
(e) fA is strictly decreasing on [c, d]
(f) fA (x) = 0, for all xε(d, ∞]

where, a, b, c and d are real numbers. Unless elsewhere specified, it is assumed that A is convex and bounded (i.e. -∞<a, d<∞).

Definition 2: The fuzzy number A = [a, b, c, d, ] is a trapezoidal fuzzy number if its membership function is given by:

where, : [a, b]→[0, ] and : [c, d]→[0, ] are two continuous mappings from the real line R to the closed interval [0, ]. If = 1, then A is a normal fuzzy number, otherwise, it is said to be a non-normal fuzzy number. If and are both linear, then A is referred to as a trapezoidal fuzzy number and is usually denoted by A = (a, b, c, d, ) or simply A = (a, b, c, d) if = 1. In particular, when b = c, the trapezoidal fuzzy number is reduced to a triangular fuzzy number and can be denoted by A = (a, b, c, d, ) or A = (a, b, d) if = 1. So, triangular fuzzy numbers are special cases of trapezoidal fuzzy numbers.

Fuzzy analytic hierarchy process methodology: This study adopted the extent analysis method proposed by Chang25 due to its computational simplicity. The extent analysis method is briefly discussed as follows.

Let X = {x1, x2, ..., xn} be an object set and U = {u1, u2, ..., um} be a goal set. According to Chang25, each object is taken and an extent analysis for each goal (gi) is performed, respectively. Therefore, the m extent analysis values for each object are obtained as:

where, are triangular fuzzy numbers (TFNs).

Assume that are the values of extent analysis of the ith object for m goals. The value of fuzzy synthetic extent Si is defined as25:



Let M1 = (l1, m1, u1) and M2 = (l2, m2, u2) be two TFNs, whereby the degree of possibility of M1>M2 is defined as follows25:


The membership degree of possibility is expressed as25:


where, d is the ordinate of the highest intersection point of two membership functions μM1(x) and μM2 (x), as shown in Fig. 1.

The degree of possibility for a convex fuzzy number to be greater than k convex fuzzy numbers is defined as25:


The weight vector is given by Chang25:




Via normalization, we obtain the weight vectors as25:


where, W is a non-fuzzy number.

In this present case, Chang25 method is applied to determine the level of impact of factors on WSS. We adopt a "Likert Scale" of fuzzy numbers starting from 1-9 to transform the linguistic values into TFNs, as shown in Table 1.

Fig. 1:Comparison of two fuzzy numbers

Table 1:Triangular fuzzy conversation scale


Analysis results of mainstream in da river:
Background of Da River: Da River is the largest tributary of the Red River, originating from Van Nam Province-China and flowing into Vietnam in Muong Te (Lai Chau), making the confluence with the Red River in Phu Tho. Da River valley is 52,500 km2 with the length of 910 km. The section in the territory of Vietnam covers an area of 26,800 km2 and 540 km in length.

The river flows across the north-western provinces of Vietnam including Lai Chau, Dien Bien, Son La, Hoa Binh and Phu Tho. The rivers and streams in the Da River basin have narrow valley and the river beds are being seriously dug up, with many rapids. The average altitude of the river valley is 1,130 m, particularly the section in the territory of Vietnam is 965 m. Beside the mainstream, Da River has six river branches including Nam Po, Nam Na, Nam Muc, Nam Mu, Nam Sap and Nam Bu.

The river has a high discharge, providing 31% of the water for the Red River and the river is the great hydropower resource for Vietnam’s power industry. Along Da River, a lot of reservoirs have been built to serve the power generation, downstream flood control and enhancing the downstream flow during the dry season for irrigation and water supply. These hydroelectric power plants include "Hoa Binh", "Son La" and "Lai Chau" province. The river basin has the great resource potential with various types of rare minerals, typical ecosystems including biological sources with high biodiversity level.

Analysis result: Based on the literature review and discusion with water resource experts, three groups of risk factors causing loss of water supply security are defined as: (1) Policies and mechanisms (awareness of local people on protecting water resource is still low-PM1, lack of uniformity in policies on water resources protection-PM2, risks from Da River upstream-PM3, lack of compliance with the legislation on protecting water resources of people and business households-PM4). (2) The demand for using (the process of operating China's upstream reservoirs-DM1, increasing the demand for Da River water for industrial production-DM2, domestic waste water from local people and business households-DM3, construction of hydropower dams-DM4, toxic chemicals in agricultural production-DM5). (3) Natural factors (landslide-NF1, climate change, such as flood, drought-NF2, reducing vegetation cover-NF3, topographical and geomorphic factors causing difficulties in water supply drainage and storage-NF4, surface water scarcity-NF5).

In this study, the analytic hierarchy process was used to determine the level of impact (risk) of factor group of level 1 and level 2, which cause loss of WSS as shown in Fig. 2.

Calculation procedure is implemented according to the steps below:
Determining the comparative relationship between pairs of factors: Based on the results of the survey of 50 water resources experts, the average comparison values between the factor pairs of level 1 and 2 are presented in Table 2-5. Particularly, Table 2 shows the average comparative values between factor groups of level 1 using the likert scale in Table 1 and survey from 50 experts. Table 2 indicates that in factor groups of level 1, the average comparative values of the factors PM, NF are larger than the factors DM and PM, DM, respectivly. In Table 3, for the demand factors group: The average comparative values of DM1 is larger than DM3, DM2 is larger than DM1 and DM4, DM4 is larger than DM3 and DM5 is larger than DM1-4.

Fig. 2: Analytic hierarchy process for determining the importance weight of factors

Table 2:Average comparative value between factor groups of level 1
PM: Policies and mechanisms, NF: Natural factors, DM: Demand factors

Table 3: Average comparative values of demand factors

Table 4: Average comparative values of policies and mechanisms

Table 5: Average comparative values of natural factors

Table 6: Aggregate values of factors of level 1 and 2

Table 7: Possibility of comparative relationship between the factor groups of level 1

Table 4 shows that the average comparative values of PM1 is larger than PM2 and PM3, PM3 is larger than PM2, PM4 is larger than PM1-3. For the natural factors group, the results in Table 5 indicate that the average comparative values of NF1 is larger than NF3, NF2 is larger than NF1, 3-4, NF4 is larger than NF1, 3, NF5 is larger than NF1-4.

Calculating the aggregate value for each factor: Using equations 1 and 2, the aggregate values of each factor are presented in Table 6.

Calculating the posibility of comparative relation between 2 fuzzy numbers: Table 7-10 indicate the posibility of comparative relation between two fuzzy numbers using Eq. 3 and 4. From Table 7, we can see that the possibilities of natural factors are better than policies and mechanisms, policies and mechanisms are better than demand factors, natural factors are better than demand factors are 100%. In Table 8, the factor PM1 is better than the factors PM2 and PM3 with the possibilities of 100%, the factor PM3 is better than the factors PM2 and PM4 with the possibilities of 100%, the factor PM4 is better than the factors PM1, PM2 and PM3 also with the possibilities of 100%.

Table 8:
Possibility of comparative relationship between the factors group of level 2-mechanisms and policies

Table 9: Possibility of comparative relationship between the factor group of level 2-use demand

Table 10: Possibility of comparative relationship between the factor group of level 2-natural elements

Table 11:
Possibility of one-factor relationship that is better than the other factors

Table 9 shows that the possibilities of one factor is better than other factors between DM1 and DM3, DM2 and DM1-3, DM3 and DM4, DM4 and DM1,3,5, DM5 and DM1-4 are highest in the demand factors. In Table 10, the possibility that NF1 is better than NF2 is 100%, the possibility that NF2 is better than NF1, 2, 4 is 100%, the possibility that NF3 is better than NF4 is 100%, the possibility that NF4 is better than NF1, 3, 5 is 100% and the possibility that NF5 is better than NF1-4 is 100%.

Calculating the possibility of the one-factor relationship better than the other factors: Using Table 7-10 and Eq. 5 and 6, it is possible to determine the possibility of one-factor relationship which is better than the other factor as shown in the Table 11.

Table 12:
Risk level of factors influencing the security of water sources

Determining the risk level of factors: The risk level of the factor groups is determined based on the Table 11 and Eq. 7. Table 12 presents the risk level of the factors.


The present study applies fuzzy AHP method to determine the important factors which influence the security of water sources in the mainstream of the "Da". The findings of this study should be of interest to both researchers and policy makers in order to manage the water resource1,2,6. The results of the Table 12 show that the risk affecting the WSS from the natural factor group is the highest, followed by the mechanism and policy factor group and the use demand factor group. This results are almost the same in the case of China as mentioned by Wang et al.8 and Jiang10. Specifically, in the natural factor group such as climate change and dependence on natural water sources are identified as the highest levels of impact on WSS in the region. They are followed by groundwater and natural water levels as well as herbage covering deterioration. In Vietnam, more than 60% of surface water discharge comes from outside the country and the Mekong and "Da" rivers (also called "Hong" river) are projected to be inundated because of climate change, a one-meter rise in sea level would cost Vietnam 5% of its land28. Cosslett et al.29 also indicated that climate change and extreme weather events have the strongest impacts in WSS in Mekong delta.

For the policy and mechanism factor group: the greatest risk affecting the WSS is the lack of mechanisms and policies on coordination with China in the use of Da River’s water and the people’s bad awareness in the water source protection, followed by the lack of uniformity in water source protection policy and lack of local government management in water use, chemicals for agricultural production. Asian Development Bank (ADB)30 indicated that urban centers in many countries in Asia and the Pacific still fall short of the vision of water underpinning vibrant, livable cities and towns. And in most regions, only a small portion of wastewater is collected through an improved sanitation method (i.e. Vietnam is 10%, the Philippines is 4% and Indonesia is 1%).

In the use demand factor groups: The greatest risk of WSS in the area is due to the construction of hydropower dams, followed by toxic chemicals in agricultural production, the increase in the use demand of Da River’s water for industrial production and the lack of clean domestic water and domestic wastewater of local residents and business households14.


The research applies the hierarchical analysis method to determine the level of impact of factors on WSS. The results of analysis show that the greatest risk group affecting the WSS is the natural factor group, followed by the mechanism and policy factor group and use demand factor group. The results obtained from this study may serve as a reference for policy makers in water resources management.


In this study, the fuzzy Analytic Hierarchy Process (AHP) technique was applied to quantify the factors which influence WSS. This study discovers that the climate change and dependence on natural water sources factor in group of natural factors have the highest effect to WSS, followed by the group of mechanism and policy factors and group of water demand factors. This study will help policy makers in order to recommend a legal tool in order to protect and guarantee water supply security in Vietnam. Thus, a new polices or law on WSS may be developed.

ADB., 2016. Asian Water Development Outlook 2016: Strengthening Water Security in Asia and the Pacific. Asian Development Bank, Manila, Philippines, ISBN-13: 9789292575434, Pages: 136.

Akaa, O.U., A. Abu, M. Spearpoint and S. Giovinazzi, 2016. A group-AHP decision analysis for the selection of applied fire protection to steel structures. Fire Saf. J., 86: 95-105.
CrossRef  |  Direct Link  |  

Al-Saidi, M., 2017. Conflicts and security in integrated water resources management. Environ. Sci. Policy, 73: 38-44.
CrossRef  |  Direct Link  |  

Anane, M., L. Bouziri, A. Limam and S. Jellali, 2012. Ranking suitable sites for irrigation with reclaimed water in the Nabeul-Hammamet region (Tunisia) using GIS and AHP-multicriteria decision analysis. Resour. Conserv. Recycl., 65: 36-46.
CrossRef  |  Direct Link  |  

Aschilean, I., G. Badea, I. Giurca, G.S. Naghiu and F.G. Iloaie, 2017. Choosing the optimal technology to rehabilitate the pipes in water distribution systems using the AHP method. Energy Procedia, 112: 19-26.
CrossRef  |  Direct Link  |  

Bian, T., J. Hu and Y. Deng, 2017. Identifying influential nodes in complex networks based on AHP. Physica A: Stat. Mech. Applic., 479: 422-436.
CrossRef  |  Direct Link  |  

Bitterman, P., E. Tate, K.J. van Meter and N.B. Basu, 2016. Water security and rainwater harvesting: A conceptual framework and candidate indicators. Applied Geogr., 76: 75-84.
CrossRef  |  Direct Link  |  

Chang, D.Y., 1996. Applications of the extent analysis method on fuzzy AHP. Eur. J. Operat. Res., 95: 649-655.
CrossRef  |  Direct Link  |  

Cosslett, T.L. and P.D. Cosslett, 2013. Water Resources and Food Security in the Vietnam Mekong Delta. Springer, New York, USA., ISBN-13: 9783319021980, Pages: 178.

Dong, Q.J. and X. Liu, 2014. Risk assessment of water security in Haihe River Basin during drought periods based on DS evidence theory. Water Sci. Eng., 7: 119-132.
CrossRef  |  Direct Link  |  

Dubois, D. and H. Prade, 1978. Operations on fuzzy numbers. Int. J. Syst. Sci., 9: 613-626.
CrossRef  |  Direct Link  |  

Grey, D. and C.W. Sadoff, 2007. Sink or swim? Water security for growth and development. Water Policy, 9: 545-571.
CrossRef  |  Direct Link  |  

Hillerman, T., J.C.F. Souza, A.C.B. Reis and R.N. Carvalho, 2017. Applying clustering and AHP methods for evaluating suspect healthcare claims. J. Comput. Sci., 19: 97-111.
CrossRef  |  Direct Link  |  

Holding, S., D.M. Allen, C. Notte and N. Olewiler, 2015. Enhancing water security in a rapidly developing shale gas region. J. Hydrol.: Regional Stud., (In Press). 10.1016/j.ejrh.2015.09.005

Jiang, Y., 2015. China's water security: Current status, emerging challenges and future prospects. Environ. Sci. Policy, 54: 106-125.
CrossRef  |  Direct Link  |  

Karahalios, H., 2017. The application of the AHP-TOPSIS for evaluating ballast water treatment systems by ship operators. Transp. Res. Part D: Transp. Environ., 52: 172-184.
CrossRef  |  Direct Link  |  

Kaufmann, A. and M.M. Gupta, 1991. Introduction to Fuzzy Arithmetic: Theory and Applications. Van Nostrand Reinhold Co., New York, USA., ISBN-13: 9780442008994, Pages: 361.

Kim, N., J. Park and J.J. Choi, 2017. Perceptual differences in core competencies between tourism industry practitioners and students using Analytic Hierarchy Process (AHP). J. Hosp. Leisure Sport Tourism Educ., 20: 76-86.
CrossRef  |  Direct Link  |  

Le, N.T., N.M. Cuong, N.V. Loc, N.T. Tuan and N.N. Mien, 2017. Applying the structural equation modeling in the determination of risk groups causing water resource insecurity. Int. J. Scient. Res., 2: 7-19.
Direct Link  |  

Misra, A.K., 2014. Climate change and challenges of water and food security. Int. J. Sustain. Built Environ., 3: 153-165.
CrossRef  |  Direct Link  |  

Sindhu, S., V. Nehra and S. Luthra, 2017. Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India. Renew. Sustain. Energy Rev., 73: 496-511.
CrossRef  |  Direct Link  |  

Siska, E.M. and K. Takara, 2015. Achieving water security in global change: Dealing with associated risk in water investment. Procedia Environ. Sci., 28: 743-749.
CrossRef  |  Direct Link  |  

Srinivasan, V., M. Konar and M. Sivapalan, 2017. A dynamic framework for water security. Water Secur., (In Press). 10.1016/j.wasec.2017.03.001

Veettil, A.V. and A.K. Mishra, 2016. Water security assessment using blue and green water footprint concepts. J. Hydrol., 542: 589-602.
CrossRef  |  Direct Link  |  

Vietnomics, 2010. Hydroelectric power and water security in Vietnam.

WWAP., 2015. The United Nations World Water Development Report 2015: Water for a Sustainable World. UNESCO Publishing, Paris, France, ISBN-13: 9789231000713, Pages: 122.

Wagener, T., M. Sivapalan, P.A. Troch, B.L. McGlynn and C.J. Harman et al., 2010. The future of hydrology: An evolving science for a changing world. Water Resour. Res., Vol. 46, No. 5. 10.1029/2009WR008906

Wang, J., Y. Li, J. Huang, T. Yan and T. Sun, 2017. Growing water scarcity, food security and government responses in China. Global Food Secur., (In Press). 10.1016/j.gfs.2017.01.003

Zhang, N., K. Zhou and X. Du, 2017. Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang, NW China. J. Afr. Earth Sci., 128: 84-96.
CrossRef  |  Direct Link  |  

Zyoud, S.H., L.G. Kaufmann, H. Shaheen, S. Samhan and D. Fuchs-Hanusch, 2016. A framework for water loss management in developing countries under fuzzy environment: Integration of fuzzy AHP with fuzzy TOPSIS. Expert Syst. Applic., 61: 86-105.
CrossRef  |  Direct Link  |  

©  2017 Science Alert. All Rights Reserved
Fulltext PDF References Abstract