# Integrated Fuzzy AHP-TOPSIS Model for Assessing Managed Aquifer Recharge Potential in a Hot Dry Region: A Case Study of Djibouti at a Country Scale

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}(Figure 2). The country has a current estimated population of one million and shares terrestrial borders with Eritrea, Ethiopia, and Somalia, as well as maritime borders with Yemen. The climate of Djibouti is characterized by hot and arid conditions, with an annual mean rainfall of 150 mm and temperatures ranging from 20 °C to 30 °C in winter (October to April) and 30 °C to 45 °C in summer (May to September) [43]. In Djibouti, the principal groundwater reserves consist of fractured volcanic aquifers, primarily represented by three geological formations: the Dalha basalts, the Stratoid basalts, and the Mabla rhyolites [44,45]. These geological formations serve as the main sources of groundwater supply, characterized by a fractured nature and high permeability, allowing for substantial quantities of groundwater to be extracted. Furthermore, sedimentary aquifers with a transmissivity between 5.6 × 10

^{−6}and 1.3 × 10

^{−3}m

^{2}/s [44] are present along some watersheds and the eastern coastal part of the study region.

#### 2.2. Data Source and Layer Processing

**Table 1.**List of selected decision criteria, accompanied by their corresponding descriptions, along with the ratings assigned to the criteria classes.

Clusters | Criteria | ID | Classes | Rating | Source Reference |
---|---|---|---|---|---|

Surface | Slope ^{a} (%) | S | 0–2 | 5 | AW3D [47] |

2–5 | 4 | ||||

5–10 | 3 | ||||

10–25 | 2 | ||||

>25 | 1 | ||||

Soil texture ^{b} | ST | Eutric Fluvisols (FLeu) | 5 | HWSD v2.0 [48] | |

Petric Gypsisols (GYp) | 4 | ||||

Eutric Leptosols (LPeu) | 2 | ||||

Lithic Leptosols (LPli) | 2 | ||||

Haptic Solonchaks (SCh) | 1 | ||||

Curve number ^{c} | CN | 75–85 | 5 | Jaafar and Ahmad [49] | |

67–75 | 4 | ||||

62–67 | 3 | ||||

55–62 | 2 | ||||

40–55 | 1 | ||||

Environment | Rainfall ^{d} (mm) | R_{l} | 153–220 | 5 | Dabar et al. [50] |

134–153 | 4 | ||||

118–134 | 3 | ||||

95–118 | 2 | ||||

43–95 | 1 | ||||

Normalized difference ^{e} | NDVI | 0.10–0.24 | 5 | Sentinel 2 [56] | |

vegetation index | 0.05–0.10 | 4 | |||

0.02–0.05 | 3 | ||||

0–0.02 | 1 | ||||

Drainage density ^{f} (km/km^{2}) | DD | 0–4.13 | 5 | AW3D [47] | |

4.13–7.10 | 4 | ||||

7.10–10.17 | 3 | ||||

10.17–14.12 | 2 | ||||

14.12–24.47 | 1 | ||||

Subsurface | Depth to groundwater ^{g} (m) | DG | 0.7–13 | 1 | MAEPE-RH |

13–21 | 5 | ||||

21–35 | 3 | ||||

35–67 | 2 | ||||

>67 | 1 | ||||

Geology ^{h} | G | Sedimentary formations | 5 | MAEPE-RH [46] | |

Recent basalt (1 Ma-actual) | 3 | ||||

Stratoid basalt (3.4–1 Ma) | 2 | ||||

Golf basalt (3.4–1 Ma) | 2 | ||||

Somali basalt (9–3.4 Ma) | 2 | ||||

Dalha basalt (9–3.4 Ma) | 2 | ||||

Adolei basalt (25Ma) | 2 | ||||

Stratoid rhyolite | 1 | ||||

Mabla rhyolite | 1 | ||||

Cretaceous–Jurassic base | 1 | ||||

Groundwater quality ^{i} (µS/cm) | EC | 200–1000 | 5 | MAEPE-RH | |

1000–2100 | 4 | ||||

2100–2600 | 3 | ||||

2600–3700 | 2 | ||||

>3700 | 1 |

^{a}Slope serves as a highly utilized decision criterion in the context of MAR mapping due to its significant influence on the convergence and divergence of runoff water, ultimately affecting the infiltration capacity [24].

^{b}Rajasekhar et al. [57] noted that the soil type plays a critical role in regulating both infiltration rates and the potential generation of runoff. Specifically, soils with a high clay content tend to display diminished infiltration rates and increased runoff, whereas soils with a high sand content tend to exhibit enhanced infiltration rates and reduced runoff.

^{c}As a dimensionless index that characterizes the soil’s ability to absorb water, the curve number can be used to indirectly estimate the volume of runoff that can be harvested in a particular area for MAR usage [58].

^{d}Rainfall plays a pivotal role in MAR mapping since rainwater represents the main source of water for MAR projects worldwide [59].

^{e}According to Ansems et al. [60], high-NDVI regions could be indicative of the temporal availability of water and thereby have the potential to reclaim large volumes of water.

^{f}Drainage density is inversely proportional to permeability such that areas with a high drainage density indicate the presence of low-permeability rock, whereas a low drainage density suggests the presence of more permeable rock [61].

^{g}The depth to groundwater influences the feasibility of the MAR project as well as the recharge rates [20].

^{h}The rock type prevalent in a given region plays a vital role in governing the movement and distribution of groundwater [62].

^{i}Injecting reclaimed water into a poor-groundwater-quality region could jeopardize the MAR benefit; therefore, it is important to include groundwater quality parameters in the decision framework [58].

#### 2.3. MCDA Rationale

#### 2.3.1. Description of the Fuzzy AHP Algorithm

- After the hierarchical structure is revealed, decision-makers construct binary comparison matrices in accordance with their perspectives. These matrices encompass the relative evaluations and favored choices among components at each level of the hierarchy. The reciprocals of linguistic variables regarding the importance degrees of the criteria are incorporated into the preferences of the experts who attended the surveys. Thus, ${l}_{ij},{m}_{ij}$, and ${u}_{ij}$, indicating the lower, mean, and upper widths of the pairwise judgments of the experts for criterion i compared to criterion j, respectively, are determined (Table 2).
- In the fuzzy AHP approach, an additional step is implemented to verify the consistency of experts’ pairwise comparisons. This is achieved by calculating the consistency ratio (CR), where CR values exceeding 0.1 indicate inconsistent judgments made by respondents, while CR values below the threshold indicate a more consistent set of expert preferences. The following expression can be utilized to determine the CR values, thus assessing the level of consistency in the decision-making process.$$CR=\frac{\frac{{\lambda}_{max}-n}{n-1}}{RI}$$
- In this step, the fuzzy equivalents of each linguistic variable are calculated. Equation (3) outlines the method for determining the lower (${l}_{ijk}$), mean (${m}_{ijk}$), and upper (${u}_{ijk}$) widths of the fuzzy equivalents using the triangular membership function.$${l}_{ij}={\left({\displaystyle \prod}_{k=1}^{K}{l}_{ijk}\right)}^{1/K};{m}_{ij}={\left({\displaystyle \prod}_{k=1}^{K}{m}_{ijk}\right)}^{1/K};{u}_{ij}={\left({\displaystyle \prod}_{k=1}^{K}{u}_{ijk}\right)}^{1/K}$$in which $K$ is the total number of respondents.
- To address the inherent vagueness and uncertainty in experts’ judgments (Table 4), Chang’s [75] extent analysis was employed. In this approach, crisp mathematical notations were utilized to obtain fuzzy quantities. The object set, represented by $X=\left\{{x}_{1},{x}_{2},{x}_{3},\dots ,{x}_{n}\right\}$, and the goal set, denoted by $U=\left\{{u}_{1},{u}_{2},{u}_{3},\dots ,{u}_{n}\right\}$, were considered in the extent analysis. For each goal, denoted by ${u}_{i}$, extent analysis values represented by m are obtained for each object.$${l}_{ij}={\left({\displaystyle \prod}_{k=1}^{K}{l}_{ijk}\right)}^{1/K};{m}_{ij}={\left({\displaystyle \prod}_{k=1}^{K}{m}_{ijk}\right)}^{1/K};{u}_{ij}={\left({\displaystyle \prod}_{k=1}^{K}{u}_{ijk}\right)}^{1/K}$$To calculate ${M}_{gi}^{j}$, the fuzzy extent analysis M value addition operation is performed on the matrix. This operation involves adding each triangular fuzzy number (TFN) in each row of the matrix using the addition operation, as described in Equation (5).$$\sum}_{j=1}^{m}{M}_{gi}^{j}=\left({\displaystyle \sum}_{j=1}^{m}{l}_{j},{\displaystyle \sum}_{j=1}^{m}{m}_{j},{\displaystyle \sum}_{j=1}^{m}{u}_{j}\right)$$with $i=1,2,\dots ,n$. The score ${\left[{{\displaystyle \sum}}_{j=1}^{n}{{\displaystyle \sum}}_{j=1}^{m}{M}_{gi}^{j}\right]}^{-1}$ is obtained by calculating the sum of the entire triangular fuzzy number set ${M}_{gi}^{j}\left(j=1,2,\dots ,m\right)$.$$\left[{{\displaystyle \sum}}_{j=1}^{n}{{\displaystyle \sum}}_{j=1}^{m}{M}_{gi}^{j}\right]=\left[{{\displaystyle \sum}}_{j=1}^{n}{{\displaystyle \sum}}_{j=1}^{m}{l}_{j},{{\displaystyle \sum}}_{j=1}^{n}{{\displaystyle \sum}}_{j=1}^{m}{m}_{j},{{\displaystyle \sum}}_{j=1}^{n}{{\displaystyle \sum}}_{j=1}^{m}{u}_{j}\right]$$The inverse of the initial equation can be computed using the formula presented in Equation (7).$${\left[{{\displaystyle \sum}}_{j=1}^{n}{{\displaystyle \sum}}_{j=1}^{m}{M}_{gi}^{j}\right]}^{-1}=\left(\frac{1}{{{\displaystyle \sum}}_{i=1}^{n}{u}_{1}},\frac{1}{{{\displaystyle \sum}}_{i=1}^{n}{m}_{1}},\frac{1}{{{\displaystyle \sum}}_{i=1}^{n}{l}_{1}}\right)$$A comparative calculation is performed to assess the level of possibility between fuzzy numbers. This comparison is utilized to determine the weight value assigned to each criterion. When comparing two triangular fuzzy numbers ${M}_{1}=\left({l}_{1},{m}_{1},{u}_{1}\right)$ and ${M}_{2}=\left({l}_{2},{m}_{2},{u}_{2}\right)$, where the probability level S
_{2}≥ S_{1}, a definition can be established.$$\left({M}_{2}\ge {M}_{1}\right)=\left\{\begin{array}{cc}1,& if{m}_{1}\ge {m}_{2}\\ 0,& if{l}_{1}\ge {l}_{2}\\ \frac{{l}_{1}-{u}_{2}}{\left({m}_{2}-{u}_{2}\right)-\left({m}_{1}-{l}_{1}\right)},& forothers\end{array}\right.$$To compare ${M}_{1}$ and ${M}_{2}$, it is necessary to calculate the values V (${M}_{1}$ ≥ ${M}_{2}$) and V (${M}_{2}$ ≥ ${M}_{1}$). Once the fuzzy synthetic values have been compared, the minimum value is determined using Equation (9).$${D}^{\prime}\left({A}_{i}\right)=minV\left({S}_{i}\ge {S}_{k}\right)$$For each k value ranging from 1 to n, where k $\ne $i, the weight vector is calculated to facilitate the interpretation of the defined criteria.$${W}^{\prime}={\left[{d}^{\prime}\left({A}_{1}\right),{d}^{\prime}\left({A}_{2}\right),\dots ,{d}^{\prime}\left({A}_{n}\right)\right]}^{T}$$where ${A}_{i}\left(i=1,2,\dots n\right)$ is n elements, and d’(${A}_{i}$) is the score describing each decision attribute of the compared options.

- 5.
- The last step is considered crucial in determining the degrees of importance for the considered criteria [76]. Therefore, a sensitivity analysis was conducted to examine the variations in criteria importance based on different degrees of fuzziness. The initial degree of fuzziness in the adopted FAHP method was set to 1, determined by the distances between l, m, and u values (Table 2). Additionally, five additional fuzziness degrees (1.2, 1.4, 1.6, 1.8, and 2.0) were explored in the current study. Consequently, if the order of importance remains relatively unchanged, it can be concluded that the decision analysis framework yields reliable results and is not significantly influenced by changes in fuzziness degrees [77].

#### 2.3.2. Description of the TOPSIS Algorithm

- Defining the Decision Matrix: A decision matrix is formulated, encompassing all available alternatives along with their corresponding performance values on various criteria. The decision matrix is typically represented as an m × n matrix, where m is the number of alternatives and n is the number of criteria. The decision matrix according to the TOPSIS method is shown in Equation (12).$${A}_{ij}=\left[\begin{array}{cccc}{a}_{11}& {a}_{12}& \dots & {a}_{1n}\\ {a}_{21}& {a}_{22}& \dots & {a}_{2n}\\ .& .& .& .\\ .& .& .& .\\ {a}_{m1}& {a}_{m2}& \dots & {a}_{mn}\end{array}\right]$$In this study, the generation of the decision matrix involved several steps. Initially, a rectangular grid layer with a spatial resolution of 500 m × 500 m was created and subsequently clipped with the study area vector layer to confine the analysis within the defined study boundaries. Then, the zonal statistics tool was employed to calculate the mean value for each pixel, considering the nine decision layers as input raster layers. Consequently, the outcome of this process yielded a decision matrix consisting of 90,177 rows and 9 columns.
- Normalizing the Decision Matrix: The decision matrix is normalized to eliminate any scale differences among the criteria. This step ensures that all criteria are given equal weightage in the decision-making process. Various normalization methods can be used, such as min–max normalization or vector normalization. The normalization of the decision matrix can be calculated through the formula depicted in Equation (13).$${r}_{ij}=\frac{{a}_{ij}}{\sqrt{{{\displaystyle \sum}}_{k=1}^{m}{a}_{kj}^{2}}}\left(i=1,2,\dots ,m;j=1,2,\dots ,n\right)$$
- Assigning Weights to the Criteria: The relative importance or weights of the criteria are determined. The weights reflect the significance of each criterion in the decision-making process. The determination of weights can be subjective, based on expert judgment, or derived using mathematical techniques, such as the analytic hierarchy process (AHP) or Entropy Weight Method. At this stage, the weighted decision matrix is obtained by multiplying the data in the normalized decision matrix, obtained in the second step of the TOPSIS method, by the weight values determined through the previously conducted weighting method. The sum of the data obtained from the weighting method must be equal to 1.
- Determining the Positive and Negative Ideal Solutions: The positive ideal solution and the negative ideal solution are identified based on the maximum and minimum values, respectively, for each criterion. The positive ideal solution represents the alternative that achieves the maximum benefit and the minimum cost, while the negative ideal solution represents the alternative that minimizes the benefit and maximizes the cost. The TOPSIS method assumes that each criterion exhibits a monotonically increasing or decreasing trend. To determine the ideal solution set, the maximum value of the column in the weighted decision matrix is selected. If the criterion is cost-oriented or in a minimization direction, the smallest criterion is chosen. The relevant formula for the ideal solution set is shown in Equation (14).$${A}^{+}=\left\{\left(max\left.{v}_{ij}\right|j\in J\right),\left(min\left.{v}_{ij}\right|j\in J\prime \right)\right\}$$In the negative ideal solution set, the smallest values of the data in the columns containing criterion values in the weighted decision matrix are examined. The formula for the negative ideal solution set is shown in Equation (15).$${A}^{-}=\left\{\left(min\left.{v}_{ij}\right|j\in J\right),\left(max\left.{v}_{ij}\right|j\in J\prime \right)\right\}$$
- Calculating Euclidean Distances: The Euclidean distance between each alternative and the positive and negative ideal solutions is calculated. The Euclidean distance represents the overall proximity or distance of each alternative to the ideal solutions in the multi-dimensional criteria space. As a result of this process, the deviation values for the alternatives are defined as the ideal separation (${S}_{1}^{+}$) and negative ideal separation $({S}_{1}^{-})$ measures. The formulas for ideal separation and negative ideal separation are shown in Equations (16) and (17).$${S}_{1}^{+}=\sqrt{{{\displaystyle \sum}}_{j=1}^{n}{\left({v}_{ij}-{v}_{j}^{+}\right)}^{2}}$$$${S}_{1}^{-}=\sqrt{{{\displaystyle \sum}}_{j=1}^{n}{\left({v}_{ij}-{v}_{j}^{-}\right)}^{2}}$$
- Calculating the Proximity to Ideal Solutions: The relative proximity of each alternative to the positive and negative ideal solutions is determined. This can be achieved by calculating the relative closeness coefficient, which is the ratio of the distance from the negative ideal solution to the sum of the distances from the positive and negative ideal solutions. The calculation of the relative closeness to the ideal solution is shown in Equation (18).$$C{C}_{i}=\frac{{S}_{1}^{-}}{{S}_{1}^{-}+{S}_{1}^{+}}$$The obtained $C{C}_{i}$ value takes a value in the range of 0 ≤ $C{C}_{i}$ ≤ 1. $C{C}_{i}$ = 1 indicates the absolute proximity of the alternative to the ideal solution, while $C{C}_{i}$ = 0 indicates the absolute proximity of the alternative to the negative ideal solution. The ranking of alternatives is determined by sorting the obtained $C{C}_{i}$ values in descending order, indicating their level of importance.

## 3. Results

#### 3.1. Criteria Weighting

#### 3.2. Sensitivity Analysis

_{l}(i.e., rainfall) was exchanged with its counterparts. The corresponding results can be explained by the fact that rainfall was originally overestimated by the experts, illustrating its high influence on the final MAR suitability decision.

#### 3.3. MAR Potential Mapping

## 4. Discussion

#### 4.1. Assessment of the Decision Criteria

#### 4.2. Assessment of the Adopted Decision Framework and Its Limitations

^{2}and, more importantly, was focused on a predefined MAR technology, namely, drywells. In contrast, this research focused on an assessment at the country scale, and rather than concentrating on a specific approach, all MAR techniques are targeted.

#### 4.3. Feasible MAR Technologies in Djibouti and Practical Utilization of the Proposed Framework

^{3}of water [103], and from the Ambouli dam, storing a considerable amount of water that can be diverted for MAR activities alongside its flood mitigation purpose. Additionally, surface-spreading techniques (such as infiltration ponds, ditches, and furrows, as well as barriers, bunds, etc.) requiring rainwater as target sources were found to be feasible for recharging local aquifers. Furthermore, drywells and flooding MAR techniques show promise in recharging shallow aquifers in the country, aiming to achieve groundwater sustainability and address recurring droughts.

## 5. Conclusions

^{2}, 5382 km

^{2}, and 7206 km

^{2}, respectively, exhibited very high, high, and moderate suitability for hosting MAR activities. Furthermore, the sensitivity analyses conducted to evaluate the stability of the framework indicated its robustness, as there were no significant changes in the ranks of the criteria with respect to the various degree-of-fuzziness values, and considerably less variation was observed in RMSE values computed based on closeness coefficients.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 3.**Thematic maps of the relevant decision criteria for MAR potential mapping: (

**a**) slope, (

**b**) soil texture, (

**c**) curve number, (

**d**) rainfall, (

**e**) normalized difference vegetation index, (

**f**) drainage density, (

**g**) depth to groundwater, (

**h**) geology, and (

**i**) electrical conductivity.

**Figure 6.**MAR suitability potential of Djibouti based on the integrated FAHP-TOPSIS approach: (

**a**) the closeness coefficient (CCi) distribution and (

**b**) the reclassified version of it.

Linguistic Variables | AHP | Fuzzy AHP | ||
---|---|---|---|---|

Importance | Value for Reciprocals | $\mathbf{Triangular}\text{}\mathbf{Fuzzy}\text{}\mathbf{Numbers}\text{}\left({\mathit{l}}_{\mathit{i}\mathit{j}},{\mathit{m}}_{\mathit{i}\mathit{j}},{\mathit{u}}_{\mathit{i}\mathit{j}}\right)$ | $\mathbf{Triangular}\text{}\mathbf{Fuzzy}\text{}\mathbf{Reciprocals}\text{}\left(1/{\mathit{u}}_{\mathit{i}\mathit{j}},1/{\mathit{m}}_{\mathit{i}\mathit{j}},1/{\mathit{l}}_{\mathit{i}\mathit{j}}\right)$ | |

Equally important | 1 | (1/1) | (1,1,1) | (1,1,1) |

Intermediate value | 2 | (1/2) | (1,2,3) | (1/3,1/2,1) |

Moderately important | 3 | (1/3) | (2,3,4) | (1/4,1/3,1/2) |

Intermediate value | 4 | (1/4) | (3,4,5) | (1/5,1/4,1/3) |

Important | 5 | (1/5) | (4,5,6) | (1/6,1/5,1/4) |

Intermediate value | 6 | (1/6) | (5,6,7) | (1/7,1/6,1/5) |

Very important | 7 | (1/7) | (6,7,8) | (1/8,1/7,1/6) |

Intermediate value | 8 | (1/8) | (7,8,9) | (1/9,1/8,1/7) |

Extremely important | 9 | (1/9) | (9,9,9) | (1/9,1/9,1/9) |

$\mathit{n}$ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|

Random Index | 0 | 0 | 0.52 | 0.89 | 1.11 | 1.25 | 1.35 | 1.40 | 1.45 | 1.49 |

ID | Sector | Job Description | Background | Experience (Years) |
---|---|---|---|---|

Expert 1 | Academia | Professor | Civil and Environmental Engineering | 16 |

Expert 2 | Academia | Associate Professor | Environmental Engineering | 11 |

Expert 3 | Municipality | Head of Department | Civil Engineering (PhD) | 20 |

Expert 4 | Municipality | Planning Engineer | Architecture (MSc) | 6 |

Expert 5 | Water administration | Unit Manager | Civil Engineering | 8 |

Expert 6 | Water administration | Technical Office Engineer | Geological Engineering (MSc) | 5 |

Expert 7 | Private sector | General Manager | Environmental Engineering (MSc) | 15 |

Expert 8 | Private sector | Modeling and Design Engineer | Geological Engineering | 6 |

Cluster | Weight | Criteria | Weight | Rank | ||
---|---|---|---|---|---|---|

Local | Global | Local | Global | |||

Surface | 30.36% | Slope | 43.92% | 13.33% | 1 | 2 |

Soil texture | 24.81% | 7.53% | 3 | 6 | ||

Curve number | 31.27% | 9.50% | 2 | 4 | ||

Environment | 48.85% | Rainfall | 62.80% | 30.68% | 1 | 1 |

Normalized difference vegetation index | 24.33% | 11.89% | 2 | 3 | ||

Drainage density | 12.86% | 6.28% | 3 | 8 | ||

Subsurface | 20.79% | Depth to groundwater | 29.32% | 6.10% | 3 | 9 |

Geology | 33.41% | 6.95% | 2 | 7 | ||

Groundwater quality (EC) | 37.27% | 7.75% | 1 | 5 |

Scenario ID | Shift | RMSE | Scenario ID | Shift | RMSE | Scenario ID | Shift | RMSE |
---|---|---|---|---|---|---|---|---|

Scenario 1 | S–ST | 0.0209 | Scenario 13 | ST–DG | 0.0062 | Scenario 25 | R_{l}–G | 0.1932 |

Scenario 2 | S–CN | 0.0259 | Scenario 14 | ST–G | 0.0014 | Scenario 26 | R_{l}–EC | 0.1519 |

Scenario 3 | S–R_{l} | 0.1416 | Scenario 15 | ST–EC | 0.0008 | Scenario 27 | NDVI–DD | 0.0242 |

Scenario 4 | S–NDVI | 0.0075 | Scenario 16 | CN–R_{l} | 0.2059 | Scenario 28 | NDVI–DG | 0.0327 |

Scenario 5 | S–DD | 0.0366 | Scenario 17 | CN–NDVI | 0.0190 | Scenario 29 | NDVI–G | 0.0213 |

Scenario 6 | S–DG | 0.0384 | Scenario 18 | CN–DD | 0.0130 | Scenario 30 | NDVI–EC | 0.0182 |

Scenario 7 | S–G | 0.0239 | Scenario 19 | CN–DG | 0.0095 | Scenario 31 | DD–DG | 0.0005 |

Scenario 8 | S–EC | 0.0246 | Scenario 20 | CN–G | 0.0154 | Scenario 32 | DD–G | 0.0028 |

Scenario 9 | ST–CN | 0.0126 | Scenario 21 | CN–EC | 0.0067 | Scenario 33 | DD–EC | 0.0037 |

Scenario 10 | ST–R_{l} | 0.1687 | Scenario 22 | R_{l}–NDVI | 0.1234 | Scenario 34 | DG–G | 0.0036 |

Scenario 11 | ST–NDVI | 0.0141 | Scenario 23 | R_{l}–DD | 0.1633 | Scenario 35 | DG–EC | 0.0046 |

Scenario 12 | ST–DD | 0.0049 | Scenario 24 | R_{l}–DG | 0.1938 | Scenario 36 | G–EC | 0.0030 |

Reference | Country | Scale | Problem | I | II | III | IV | V | VI |
---|---|---|---|---|---|---|---|---|---|

Kazakis [25] | Greece | Watershed | Saltwater intrusion | 10 | AHP | WLC | ✗ | ✗ | ✓ |

Fuentes and Vervoort [53] | Australia | Watershed | Water table decline | 9 | AHP | WLC | ✗ | ✗ | ✓ |

Sandoval and Tiburan [27] | Philippines | Watershed | Groundwater depletion | 10 | AHP | WOA | ✓ | 7 | ✗ |

Kharazi et al. [36] | Iran | Watershed | Water scarcity | 16 | N/A | AHP, TOPSIS, and EDAS | ✗ | 7 | ✗ |

Itani et al. [99] | Lebanon | Watershed | Saltwater intrusion | 9 | AHP | WLC | ✗ | 4 | ✓ |

Hussaini et al. [55] | Afghanistan | City | Water table decline | 7 | AHP and ANP | FL and WOA | ✗ | ✗ | ✗ |

Papadopoulos et al. [10] | Greece | Watershed | Excess water storage | 9 | Fuzzy AHP | FIS | ✗ | ✗ | ✗ |

Zhang et al. [31] | South Africa | Watershed | Water scarcity | 12 | AHP | WLC | ✗ | ✗ | ✗ |

Arshad et al. [61] | India | Watershed | Chemical contamination | 7 | AHP | WOA | ✗ | ✗ | ✗ |

Shadmehri Toosi et al. [32] | Iran | Watershed | Water scarcity | 6 | AHP | WLC | ✓ | ✗ | ✗ |

Ezzeldin et al. [100] | Egypt | Watershed | Water scarcity | 11 | AHP | WLC | ✗ | ✗ | ✗ |

This Study | Djibouti | Country | Water scarcity and saltwater intrusion | 9 | Fuzzy AHP | TOPSIS | ✓ | 8 | ✓ |

**I:**Number of decision criteria;

**II:**criteria weighting technique;

**III:**alternative prioritization techniques;

**IV:**experts’ details;

**V:**number of experts;

**VI:**sensitivity analysis; WOA: weighted overlay analysis; FL: fuzzy logic; FIS: fuzzy inference system.

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**MDPI and ACS Style**

Mouhoumed, R.M.; Ekmekcioğlu, Ö.; Başakın, E.E.; Özger, M.
Integrated Fuzzy AHP-TOPSIS Model for Assessing Managed Aquifer Recharge Potential in a Hot Dry Region: A Case Study of Djibouti at a Country Scale. *Water* **2023**, *15*, 2534.
https://doi.org/10.3390/w15142534

**AMA Style**

Mouhoumed RM, Ekmekcioğlu Ö, Başakın EE, Özger M.
Integrated Fuzzy AHP-TOPSIS Model for Assessing Managed Aquifer Recharge Potential in a Hot Dry Region: A Case Study of Djibouti at a Country Scale. *Water*. 2023; 15(14):2534.
https://doi.org/10.3390/w15142534

**Chicago/Turabian Style**

Mouhoumed, Rachid Mohamed, Ömer Ekmekcioğlu, Eyyup Ensar Başakın, and Mehmet Özger.
2023. "Integrated Fuzzy AHP-TOPSIS Model for Assessing Managed Aquifer Recharge Potential in a Hot Dry Region: A Case Study of Djibouti at a Country Scale" *Water* 15, no. 14: 2534.
https://doi.org/10.3390/w15142534