Groundwater Quality Assessment Using Fuzzy-AHP in An Giang Province of Vietnam

Abstract

Along with rapid population growth in Vietnam, there is an increasing dependence on groundwater for various activities. An Giang province is known to be one of the agricultural intensification areas of The Vietnamese Mekong Delta (VMD). This study aimed to evaluate the spatiotemporal variation of groundwater quality for a period of ten years from 2009 to 2018 in An Giang. The weighted groundwater quality index (GWQI) was developed based on the fuzzy analytic hierarchy process (Fuzzy-AHP) for assigning weighted parameters. The results show that that shallow wells in the Northeast and Southeast regions of An Giang were mostly categorized under "bad water" quality with high arsenic (As) concentration over the years partly due to huge amounts of sediment deposition in monsoon season. Overall, the reason for the poor groundwater quality in An Giang was the combined effect of both natural and human activities. On the other hand, we detected high values of GWQI links with high As concentration in areas where people extract more groundwater for irrigation. Temporal variation of GWQI suggested that groundwater quality at eight wells has improved from 2009 to 2018 in the wet season as compared to the dry season. The reason behind the improvement of groundwater quality during wet season was the decrease in river discharge, which causes less deposition of suspended solids near the flood plains. Moreover, the filling of unused wells can reduce the movement of pollutants from unused wells to groundwater aquifers. Although there was not sufficient evidence to show the relationship between As and sediment concentration, the temporal reduction trend in river discharge and suspended solids was detected in An Giang. The understanding of groundwater quality can help policymakers protect and manage limited water resources in the long-term.Geosciences 2019, 9, 330 2 of 23 of usages [6,7]. The disadvantages of the AHP method include the uncertainty and ambiguity in expressing opinions, as the method depends on the decision maker's knowledge and experiences during the decision-making process. Moreover, among other factors, the AHP method does not contain feedback loops [8].The fuzzy set was first developed by Zadeh in 1965 [5] and combined with Saaty's priority theory to reduce human ambiguity [9,10]. Later, the Fuzzy-AHP was further developed in order to overcome the uncertainty and ambiguity of criteria weights in deterministic and inflexible classifications [11]. Using the Fuzzy-AHP can provide a fuzzy number-interval judgment values rather than fixed or exact values [2]. This approach reduces uncertainty in assigned relative weight. As a result, the Fuzzy-AHP has been successfully used in many actual decision situation making such as energy alternatives selection [12,13], supplier selection [14], environmental sustainability evaluation [7], and water quality assessment [15][16][17]. Baghapour et al. [15] conducted the Fuzzy-AHP with fuzzy ordered weighted averaging (FOWA) for developing of the groundwater quality index (GWQI). They...

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Assessment Framework for Natural Groundwater Contamination in Arid Regions: Development of Indices and Wells Ranking System Using Fuzzy VIKOR Method

“…Defuzzified scores using Equation 16are also presented in Table 9. The defuzzified S j scores are essentially the groundwater pollution index (GWPI) defined in Equation (13). S j scores of 0.0 for "best case scenario" and 1.0 for "worst case scenario" validate the proposed methodology for the estimation of GWPI.…”

“…The first step is the estimation of importance weights of the criteria, i.e., WQPs in the proposed research. Analytical hierarchy process (AHP) is a well-known method [3,13] and found appropriate to determine the weights of the parameters arranged in the hierarchical form, as shown in Figure 3. In the second step, the transformed values of different parameters are aggregated.…”

“…The first one is the "best case scenario" with all the parameters equal to or lower than the DWQS, while the second corresponds to the "worst case scenario" with all the parameters having highest possible values held by any other well in the field. Table 9 presents the fuzzy results for S j , R j , and Q j , using the values for positive triangular ideal solution ( f * i ) and the negative triangular ideal solution ( f o i ) given in Table 4 and Equations (11) to (13). Defuzzified scores using Equation 16are also presented in Table 9.…”

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