Delineation of Groundwater Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas, Morocco
Abstract
:1. Introduction
- (1)
- One approach to mapping potential groundwater areas is data-driven and involves the use of probabilistic statistical techniques. The accuracy of the resulting forecast is influenced by both the quality and quantity of the data used [10]. Several model types have been employed in compiling these maps, including the Dempster–Shafer theory [11,12,13], frequency ratio [14,15,16], logistic regression [17,18], statistical index [12], certainty factor [19], and entropy index [20].
- (2)
- The analytic hierarchy process (AHP) is a decision-making technique that incorporates subjective opinions and evaluates multiple factors to complement decision-making. To delineate GWPA using this model, four key steps are taken: standardization of prospecting factors, generation of a pairwise comparison matrix, checking the consistency of the matrix, and weighting the evaluation factors in a GIS environment [4,10,21,22].
- (3)
- Machine learning techniques (MLT) have shown improved accuracy in many situations due to their ability to process non-linear data with varying scales and from different sources [23,24,25,26]. MLT techniques include several models, such as the aquifer sustainability factor [27], classification and regression tree [28], random forest [28,29], boosted regression tree [30], maximum entropy [31], artificial neural network model [32], and generalized additive model [33].
2. Material and Methods
2.1. Study Area
2.2. Methodology
2.2.1. Development of Decision Factor Maps
Geology Factors and Land Use
Topographic Factors
Hydrological Factors
2.2.2. Analytic Hierarchy Process Model
Standardization of Thematic Layers
Weighting of Deciding Factors
Delineation of Groundwater Potential Areas (GWPA)
2.2.3. Validation of the GWPA
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Factor (Units) | Class | Rating | Factor (Units) | Class | Rating | Factor (Units) | Class | Rating |
---|---|---|---|---|---|---|---|---|
ND | 2.71–3.39 | 10 | TPI | −19.25–(−3.87) | 10 | PGRA | 3.49–4.36 | 10 |
2.03–2.71 | 8 | −3.87–(−0.95) | 8 | 2.61–3.49 | 8 | |||
1.35–2.03 | 6 | −0.95–1.48 | 6 | 1.74–2.61 | 6 | |||
0.67–1.35 | 4 | 1.48–4.65 | 4 | 0.87–1.74 | 4 | |||
0–0.67 | 2 | 4.65–21.25 | 2 | 0–0.87 | 2 | |||
LD | 2.81–3.52 | 10 | SPI | 3.71 × 106–8.38 × 106 | 10 | S | 0–6 | 10 |
2.11–2.81 | 8 | 1.80 × 105–3.71 × 106 | 8 | 6–12 | 8 | |||
1.40–2.11 | 6 | 7.23 × 105–1.80 × 105 | 6 | 12–19 | 6 | |||
0.70–1.40 | 4 | 1.6 × 104–7.23 × 105 | 4 | 19–28 | 4 | |||
0–0.70 | 2 | 0–1.6 × 104 | 2 | 28–61 | 2 | |||
SL | 10,319–23,287 | 10 | DL/F | 0–200 | 10 | TWI | 13.53–23.86 | 10 |
4931–10,319 | 8 | 200–400 | 8 | 9.81–13.53 | 8 | |||
1826–4931 | 6 | 400–600 | 6 | 7.33–9.81 | 6 | |||
365–1826 | 4 | 600–800 | 4 | 5.68–7.33 | 4 | |||
0–368 | 2 | 800–1000 | 2 | 2.79–5.68 | 2 | |||
RP | High permeability | 10 | LU/LC | River bed | 10 | DS | 0–200 | 10 |
Medium permeability | 8 | Dense vegetation | 8 | 200–400 | 8 | |||
Lower permeability | 4 | Less-dense vegetation | 6 | 400–600 | 6 | |||
Raincoat | 2 | Rocky terrain | 4 | 600–800 | 4 | |||
DD | 24.72–30.90 | 10 | Built-up | 2 | 800–1000 | 2 | ||
18.54–24.72 | 8 | CP | Convex | 10 | ||||
12.36–18.54 | 6 | Flat | 6 | |||||
6.18–12.36 | 4 | Concave | 2 | |||||
0–6.18 | 2 |
Factors | LD | ND | PGRA | DL/F | DD | RP | DS | S | CP | LU/LC | SPI | TPI | TWI | SL |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LD | 1 | 2 | 2 | 2 | 3 | 4 | 3 | 3 | 2 | 4 | 4 | 5 | 6 | 6 |
ND | 1/2 | 1 | 2 | 2 | 3 | 4 | 2 | 3 | 3 | 5 | 6 | 6 | 6 | 6 |
PGRA | 1/2 | 1/2 | 1 | 3 | 2 | 3 | 3 | 6 | 5 | 6 | 6 | 6 | 6 | 6 |
DL/Fault | 1/2 | 1/2 | 1/3 | 1 | 2 | 2 | 2 | 4 | 2 | 3 | 4 | 5 | 4 | 4 |
DD | 1/3 | 1/3 | 1/2 | 1/2 | 1 | 3 | 2 | 2 | 6 | 4 | 2 | 4 | 5 | 3 |
RP | 1/4 | 1/4 | 1/3 | 1/2 | 1/3 | 1 | 3 | 2 | 2 | 6 | 2 | 4 | 5 | 3 |
DS | 1/3 | 1/2 | 1/3 | 1/2 | 1/2 | 1/3 | 1 | 4 | 5 | 4 | 3 | 3 | 3 | 6 |
S | 1/3 | 1/3 | 1/6 | 1/4 | 1/2 | 1/6 | 1/4 | 1 | 3 | 3 | 4 | 2 | 3 | 2 |
CP | 1/2 | 1/3 | 1/5 | 1/2 | 1/6 | 1/6 | 1/5 | 1/3 | 1 | 5 | 3 | 2 | 4 | 2 |
LU/LC | 1/4 | 1/5 | 1/5 | 1/3 | 1/4 | 1/5 | 1/4 | 1/3 | 1/5 | 1 | 6 | 6 | 6 | 6 |
SPI | 1/4 | 1/6 | 1/6 | 1/4 | 1/2 | 1/6 | 1/3 | 1/4 | 1/3 | 1/6 | 1 | 3 | 4 | 4 |
TPI | 1/5 | 1/6 | 1/6 | 1/5 | 1/4 | 1/4 | 1/3 | 1/2 | 1/2 | 1/6 | 1/3 | 1 | 2 | 3 |
TWI | 1/6 | 1/6 | 1/6 | 1/4 | 1/5 | 1/6 | 1/3 | 1/3 | 1/4 | 1/6 | 1/4 | 1/2 | 1 | 3 |
SL | 1/6 | 1/6 | 1/6 | 1/4 | 1/3 | 1/6 | 1/6 | 1/2 | 1/2 | 1/6 | 1/4 | 1/3 | 1/3 | 1 |
Factors | LD | ND | PGRA | DL/F | DD | RP | DS | S | CP | LU/LC | SPI | TPI | TWI | SL | Weight |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LD | 0.189 | 0.302 | 0.258 | 0.173 | 0.213 | 0.214 | 0.167 | 0.110 | 0.064 | 0.096 | 0.095 | 0.104 | 0.108 | 0.109 | 0.157 |
ND | 0.094 | 0.151 | 0.258 | 0.173 | 0.213 | 0.214 | 0.111 | 0.110 | 0.097 | 0.12 | 0.143 | 0.125 | 0.108 | 0.109 | 0.145 |
PGRA | 0.094 | 0.075 | 0.129 | 0.260 | 0.142 | 0.161 | 0.167 | 0.220 | 0.162 | 0.144 | 0.143 | 0.125 | 0.108 | 0.109 | 0.146 |
DL/Fault | 0.0946 | 0.0755 | 0.0431 | 0.0867 | 0.1425 | 0.1074 | 0.111 | 0.146 | 0.064 | 0.072 | 0.095 | 0.1045 | 0.072 | 0.072 | 0.092 |
DD | 0.0630 | 0.050 | 0.064 | 0.0433 | 0.0712 | 0.1611 | 0.111 | 0.073 | 0.194 | 0.096 | 0.0478 | 0.083 | 0.090 | 0.054 | 0.086 |
RP | 0.0473 | 0.0377 | 0.043 | 0.0433 | 0.0237 | 0.0537 | 0.167 | 0.073 | 0.064 | 0.144 | 0.047 | 0.083 | 0.090 | 0.054 | 0.069 |
DS | 0.0630 | 0.0755 | 0.0431 | 0.0433 | 0.035 | 0.0179 | 0.055 | 0.146 | 0.162 | 0.096 | 0.071 | 0.062 | 0.054 | 0.109 | 0.074 |
S | 0.0630 | 0.0503 | 0.0215 | 0.0216 | 0.035 | 0.008 | 0.013 | 0.036 | 0.097 | 0.072 | 0.095 | 0.041 | 0.054 | 0.036 | 0.046 |
CP | 0.0946 | 0.0503 | 0.0258 | 0.0433 | 0.0118 | 0.0089 | 0.0111 | 0.01223 | 0.0324 | 0.12 | 0.0717 | 0.0418 | 0.0722 | 0.0363 | 0.0452 |
LU/LC | 0.0473 | 0.0302 | 0.0258 | 0.0289 | 0.0178 | 0.0107 | 0.0139 | 0.0122 | 0.0064 | 0.024 | 0.1434 | 0.1254 | 0.1084 | 0.1090 | 0.0504 |
SPI | 0.0473 | 0.0251 | 0.0215 | 0.0216 | 0.0356 | 0.0089 | 0.0186 | 0.0091 | 0.01082 | 0.004 | 0.0239 | 0.0627 | 0.0722 | 0.0727 | 0.031 |
TPI | 0.0378 | 0.0252 | 0.0215 | 0.0173 | 0.0178 | 0.0134 | 0.0186 | 0.0183 | 0.0162 | 0.004 | 0.0079 | 0.0209 | 0.0361 | 0.0545 | 0.0221 |
TWI | 0.031 | 0.025 | 0.021 | 0.021 | 0.014 | 0.008 | 0.018 | 0.012 | 0.008 | 0.004 | 0.005 | 0.010 | 0.018 | 0.054 | 0.018 |
SL | 0.031 | 0.025 | 0.021 | 0.021 | 0.023 | 0.008 | 0.009 | 0.018 | 0.016 | 0.004 | 0.005 | 0.006 | 0.006 | 0.0181 | 0.015 |
λmax = 15.64 | RI = 1.52 | N = 14 | CR = 0.082 < 0.1 |
n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
---|---|---|---|---|---|---|---|---|---|---|---|
RI | 0 | 0 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.52 |
Borehole | X | Y | Total Depth | Lithological | % of the Cumul | Yield of | Permeability |
---|---|---|---|---|---|---|---|
Formation | Length Fractured Formation | Borehole (L/s) | |||||
S1 | 40,767 | 274,334 | 80 | Gd | 12% | 0.05 | - |
S2 | 41,381 | 274,956 | 32 | Gr | 60% | 3.5 | 5.3 × 10−8 |
S3 | 41,192 | 273,889 | 32 | Gr | 66% | 0.5 | 1.18 × 10−7 |
S4 | 41,107 | 273,605 | 42 | Gr | 36% | 1.8 | 1.04 × 10−6 |
S5 | 40,292 | 270,074 | 80 | Gd | 26% | 0.08 | - |
S6 | 40,209 | 270,323 | 60 | Gd | 16% | 0.02 | 1.27 × 10−6 |
S7 | 39,667 | 271,138 | 50 | Gd | 20% | - | - |
S8 | 41,829 | 271,506 | 80 | G | 32% | 0.55 | 1.07 × 10−7 |
S9 | 40,528 | 271,663 | 50 | Gd | 14% | 0.6 | 4.3 × 10−7 |
S10 | 40,998 | 27,1739 | 80 | Gd | 25% | 0.45 | 0.95 × 10−7 |
S11 | 41,356 | 270,658 | 80 | G | 55% | 1.4 | - |
S12 | 40,710 | 271,121 | 50 | G | 14% | 0.06 | - |
S13 | 40,643 | 273,948 | 80 | Gd | 25% | 0.3 | 0.9 × 10−7 |
S14 | 39,380 | 273,811 | 60 | Gd | 12% | 0.02 | - |
S15 | 39,784 | 273,385 | 80 | Gd | 0% | - | - |
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Ikirri, M.; Boutaleb, S.; Ibraheem, I.M.; Abioui, M.; Echogdali, F.Z.; Abdelrahman, K.; Id-Belqas, M.; Abu-Alam, T.; El Ayady, H.; Essoussi, S.; et al. Delineation of Groundwater Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas, Morocco. Water 2023, 15, 1436. https://doi.org/10.3390/w15071436
Ikirri M, Boutaleb S, Ibraheem IM, Abioui M, Echogdali FZ, Abdelrahman K, Id-Belqas M, Abu-Alam T, El Ayady H, Essoussi S, et al. Delineation of Groundwater Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas, Morocco. Water. 2023; 15(7):1436. https://doi.org/10.3390/w15071436
Chicago/Turabian StyleIkirri, Mustapha, Said Boutaleb, Ismael M. Ibraheem, Mohamed Abioui, Fatima Zahra Echogdali, Kamal Abdelrahman, Mouna Id-Belqas, Tamer Abu-Alam, Hasna El Ayady, Sara Essoussi, and et al. 2023. "Delineation of Groundwater Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas, Morocco" Water 15, no. 7: 1436. https://doi.org/10.3390/w15071436
APA StyleIkirri, M., Boutaleb, S., Ibraheem, I. M., Abioui, M., Echogdali, F. Z., Abdelrahman, K., Id-Belqas, M., Abu-Alam, T., El Ayady, H., Essoussi, S., & Faik, F. (2023). Delineation of Groundwater Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas, Morocco. Water, 15(7), 1436. https://doi.org/10.3390/w15071436