Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)
"> Figure 1
<p>Location of study area in Semnan Province and Iran (inset) and the locations of training and validation wells in the study area.</p> "> Figure 2
<p>Flowchart of the research.</p> "> Figure 3
<p>Location of random points in the study area.</p> "> Figure 4
<p>Groundwater conditioning factors. (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) stream power index (SPI), (<b>e</b>) topography position index (TPI), (<b>f</b>) topography wetness index (TWI), (<b>g</b>) terrain ruggedness index (TRI), (<b>h</b>) convergence index (CI), (<b>i</b>) distance to stream, (<b>j</b>) distance to road, (<b>k</b>) distance to fault, (<b>l</b>) drainage density, (<b>m</b>) rainfall, (<b>n</b>) soil type, (<b>o</b>) land use/land cover (LULC), (<b>p</b>) lithology, (<b>q</b>) normalized difference vegetation index (NDVI).</p> "> Figure 4 Cont.
<p>Groundwater conditioning factors. (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) stream power index (SPI), (<b>e</b>) topography position index (TPI), (<b>f</b>) topography wetness index (TWI), (<b>g</b>) terrain ruggedness index (TRI), (<b>h</b>) convergence index (CI), (<b>i</b>) distance to stream, (<b>j</b>) distance to road, (<b>k</b>) distance to fault, (<b>l</b>) drainage density, (<b>m</b>) rainfall, (<b>n</b>) soil type, (<b>o</b>) land use/land cover (LULC), (<b>p</b>) lithology, (<b>q</b>) normalized difference vegetation index (NDVI).</p> "> Figure 5
<p>Relative importance of conditioning factors using random forest model.</p> "> Figure 6
<p>Weighted GWCFs using the FR model for (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) stream power index (SPI), (<b>e</b>) topography position index (TPI), (<b>f</b>) topography wetness index (TWI), (<b>g</b>) terrain ruggedness index (TRI), (<b>h</b>) convergence index (CI), (<b>i</b>) distance to stream, (<b>j</b>) distance to road, (<b>k</b>) distance to fault, (<b>l</b>) drainage density, (<b>m</b>) rainfall, (<b>n</b>) soil type, (<b>o</b>) LU/LC, (<b>p</b>) lithology, (<b>q</b>) NDVI.</p> "> Figure 6 Cont.
<p>Weighted GWCFs using the FR model for (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) stream power index (SPI), (<b>e</b>) topography position index (TPI), (<b>f</b>) topography wetness index (TWI), (<b>g</b>) terrain ruggedness index (TRI), (<b>h</b>) convergence index (CI), (<b>i</b>) distance to stream, (<b>j</b>) distance to road, (<b>k</b>) distance to fault, (<b>l</b>) drainage density, (<b>m</b>) rainfall, (<b>n</b>) soil type, (<b>o</b>) LU/LC, (<b>p</b>) lithology, (<b>q</b>) NDVI.</p> "> Figure 7
<p>Groundwater potential map using the VIKOR-RF ensemble model.</p> "> Figure 8
<p>Percentage of potential classes.</p> "> Figure 9
<p>Area under curve (AUC). (<b>a</b>) Prediction rate curve (PRC), (<b>b</b>) Success rate curve (SRC).</p> "> Figure 10
<p>Trend of frequency ratio and seed cell area index indicators.</p> ">
Abstract
:1. Introduction
- Data mining/machine learning models, like boosted regression tree (BRT) [37], classification and regression tree (CART) [38], multivariate adaptive regression splines (MARS) [37], artificial neural network (ANN) [39], random forest (RF) [40], fuzzy logic (FL) [41], Fisher’s linear discriminant function (FLDA) [42], support vector machine (SVM) [43], logistic model tree (LMT) [44], K-nearest neighbor (KNN) [45], and quadratic discriminant analysis [46].
2. Materials and Methods
2.1. Study Area
2.2. Methodology
2.3. Data Preparation
2.4. Models
2.4.1. Frequency Ratio (FR)
2.4.2. VIKOR (Vlse Kriterijumsk Optimizacija Kompromisno Resenje)
- (1)
- Prepare the decision matrix.
- (2)
- Calculate the normalized matrix, as shown in Equation (6):
- (3)
- Calculate a weighted normalized matrix, as shown in Equation (7):
- (4)
- Identify the ideal positive (Equation (8)) and negative (Equation (9)) options:
- (5)
- Calculate the utility index (Equation (10)) and incompatibility index (the distance from positive and negative ideal solution) (Equation (11)):
- (6)
- Calculate the VIKOR index and determine the final weight of the alternatives, as shown in Equation (12):
2.4.3. Random Forest
- ○
- Considers the number of tests (N) and the number of variables (M),
- ○
- Enters R(m) variables to decide on each tree node (m should be less than M),
- ○
- Selects test data for the tree by using the n-times placement of N samples, and the rest of the samples are used to estimate the tree-error,
- ○
- Selects M variables for each tree node, the basis for decision making in each node. The best groups are calculated on m variables in the test-sample, and
- ○
- Expands each tree completely without pruning.
2.5. Validation of Results
3. Results
3.1. Multi-Collinearity Analysis
3.2. Determining the Relative Importance of GWCFs using the RF Model
3.3. Determining the Value of Each Pixel of GWCFs using the FR Model
3.4. Application of VIKOR Model
3.5. Groundwater Potential Mapping Using the FR-RF-VIKOR Ensemble Model
3.6. Validation of Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Factors | Min | Max | Classes | Methods |
---|---|---|---|---|
Elevation (m) | 1357 | 3893 | 1) <1659, 2) 1659–1990, 3) 1990–2342, 4) 2342–2724, 5) 2724–3163, 6) >3163 | Natural break (Jenks) |
Slope (°) | 0 | 70.66 | 1) <5, 2) 5–10, 3) 10–15, 4) 15–20, 5) 20–30, 6) >30 | Natural break (Jenks) |
Aspect | −1 | 337.5 | 1) Flat (−1), 2) North (0–22.5), 3) Northeast (22.5–67.5), 4) East (67.5–112.5), 5) Southeast (112.5–157.5), 6) South (157.5–202.5), 7) Southwest (202.5–247.5), 8) West (247.5–292.5), 9) Northwest (292.5–337.5) | Directional units |
SPI | 6.27 | 23.05 | 1) <9.16, 2) 9.16–11.07, 3) 11.07–12.85, 4) 12.85–15.35, 5) >15.35 | Natural break (Jenks) |
TPI | −63.2 | 70.6 | 1) <−10.74, 2) −10.74–−3.3, 3) −3.3–2.9, 4) 2.9 0 11.8, 5) >11.8 | Natural break (Jenks) |
TWI | 1.20 | 21.04 | 1) <4.86, 2) 4.86–7.27, 3) 7.27–10.85, 4) >10.85 | Natural break (Jenks) |
TRI | 0 | 63.1 | 1) <3.22, 2) 3.22–7.43, 3) 7.43–11.64, 4) 11.64–17.59, 5) >17.59 | Natural break (Jenks) |
CI (100/m) | −100 | 100 | 1) <−39.6, 2) −39.6–−11.3, 3) −11.3–9.01, 4) 9.01–38.03, 5) >38.03 | Natural break (Jenks) |
Distance to stream (m) | 0 | 2317.5 | 1) <100, 2) 100–200, 3) 200–300, 4) 300–400, 5) >400 | Manual |
Distance to road (m) | 0 | 11,597 | 1) <500, 2) 500–1000, 3) 1000–1500, 4) 1500–2000, 5) >2000 | Manual |
Distance to fault(m) | 0 | 16,146 | 1) <500, 2) 500–1000, 3) 1000–1500, 4) 1500–2000, 5) >2000 | Manual |
Drainage density (km/km2) | 0.15 | 3.18 | 1) <0.85, 2) 0.85–1.49, 3) 1.49–2.19, 4) >2.19 | Natural break (Jenks) |
Rainfall (mm) | 157 | 722 | 1) <248.5, 2) 248.5–350.3, 3) 350.3–461.02, 4) 461.02–582.7, 5) >582.7 | Natural break (Jenks) |
Soil type | - | - | 1) Entisols, 2) Alfisols, 3) Aridisols, 4) Entisols/Inceptisols, 5) Inceptisols, 6) Mollisols | Soil types/Orders |
LULC | - | - | 1) Agriculture, 2) Dense forest, 3) Good range, 4) Agri-dry farming, 5) Dry farming, 6) Low forest, 7) Woodland, 8) Mod-forest, 9) Mod-range, 10) Poor-range, 11) Rock, 12) Urban | Supervised Classification |
Lithology | - | - | 1) A, 2) B, 3) C, 4) D, 5) E, 6) F, 7) G, 8) H, 9) I, 10) J. | Lithological Units |
NDVI | −0.24 | 0.54 | 1) <−0.01, 2) −0.01–0.07, 3) 0.07–0.12, 4) 0.12–0.21, 5) 0.21–0.32, 6) >0.21 | Natural break (Jenks) |
Factors | Collinearity Statistics | Factors | Collinearity Statistics | ||
---|---|---|---|---|---|
Tolerance | VIF * | Tolerance | VIF | ||
NDVI | 0.6 | 1.4 | Rainfall | 0.2 | 4.3 |
Distance to fault | 0.7 | 1.4 | Distance to road | 0.4 | 2.1 |
TRI | 0. 2 | 4.7 | Slope | 0.1 | 4.8 |
Slope aspect | 0.8 | 1.1 | Soil type | 0.3 | 2.5 |
CI | 0.7 | 1.3 | SPI | 0.5 | 1.7 |
Drainage density | 0.3 | 2.7 | Distance to stream | 0.6 | 1.4 |
Elevation | 0.3 | 3.2 | TPI | 0.3 | 2.7 |
Lithology | 0.2 | 3.4 | TWI | 0.3 | 2.7 |
LU/LC | 0.2 | 3.9 |
No Well (0) | Well (1) | |
---|---|---|
no well (0) | 74 | 6 |
well (1) | 11 | 69 |
Factor | Class | No. of Pixels | % | No. of Wells | % | FR |
---|---|---|---|---|---|---|
Elevation (m) | <1659 | 479,814 | 32.48 | 53 | 85.48 | 2.63 |
1659–1990 | 309,671 | 20.96 | 9 | 14.52 | 0.69 | |
1990–2342 | 239,854 | 16.24 | 0 | 0.00 | 0.00 | |
2342–2724 | 209,109 | 14.16 | 0 | 0.00 | 0.00 | |
2724–3163 | 146,912 | 9.95 | 0 | 0.00 | 0.00 | |
>3163 | 91,792 | 6.21 | 0 | 0.00 | 0.00 | |
Slope (°) | < 5 | 588,363 | 39.83 | 62 | 100.00 | 2.51 |
5–10 | 149,259 | 10.10 | 0 | 0.00 | 0.00 | |
10–15 | 124,159 | 8.41 | 0 | 0.00 | 0.00 | |
15–20 | 143,874 | 9.74 | 0 | 0.00 | 0.00 | |
20–30 | 280,859 | 19.01 | 0 | 0.00 | 0.00 | |
>30 | 190,637 | 12.91 | 0 | 0.00 | 0.00 | |
Aspect | F | 6262 | 0.42 | 2 | 3.23 | 7.61 |
N | 132,304 | 8.96 | 0 | 0.00 | 0.00 | |
NE | 152,509 | 10.32 | 14 | 22.58 | 2.19 | |
E | 245,198 | 16.60 | 11 | 17.74 | 1.07 | |
SE | 341,071 | 23.09 | 17 | 27.42 | 1.19 | |
S | 273,880 | 18.54 | 10 | 16.13 | 0.87 | |
SW | 144,941 | 9.81 | 5 | 8.06 | 0.82 | |
W | 89,718 | 6.07 | 2 | 3.23 | 0.53 | |
NW | 91,269 | 6.18 | 1 | 1.61 | 0.26 | |
SPI | <9.16 | 322,599 | 21.84 | 39 | 62.90 | 2.88 |
9.16–11.07 | 437,271 | 29.60 | 15 | 24.19 | 0.82 | |
11.07–12.85 | 455,113 | 30.81 | 3 | 4.84 | 0.16 | |
12.85–15.35 | 203,521 | 13.78 | 4 | 6.45 | 0.47 | |
>15.35 | 58,648 | 3.97 | 1 | 1.61 | 0.41 | |
TPI | <−10.74 | 69,120 | 4.68 | 0 | 0.00 | 0.00 |
−10.74–−3.3 | 233,435 | 15.80 | 0 | 0.00 | 0.00 | |
−3.3–2.9 | 914,956 | 61.94 | 62 | 100.00 | 1.61 | |
2.9 0 11.8 | 201,144 | 13.62 | 0 | 0.00 | 0.00 | |
>11.8 | 58,497 | 3.96 | 0 | 0.00 | 0.00 | |
TWI | <4.86 | 582,464 | 39.43 | 0 | 0.00 | 0.00 |
4.86–7.27 | 585,130 | 39.61 | 38 | 61.29 | 1.55 | |
7.27–10.85 | 237,436 | 16.07 | 18 | 29.03 | 1.81 | |
>10.85 | 72,122 | 4.88 | 6 | 9.68 | 1.98 | |
TRI | <3.22 | 692,266 | 46.86 | 62 | 100.00 | 1.61 |
3.22–7.43 | 286,708 | 19.41 | 0 | 0.00 | 0.00 | |
7.43–11.64 | 285,453 | 19.32 | 0 | 0.00 | 0.00 | |
11.64–17.59 | 174,864 | 11.84 | 0 | 0.00 | 0.00 | |
>17.59 | 37,860 | 2.56 | 0 | 0.00 | 0.00 | |
CI (100/m) | <−39.6 | 91,462 | 6.22 | 19 | 30.65 | 4.92 |
−39.6–−11.3 | 289,807 | 19.72 | 12 | 19.35 | 0.98 | |
−11.3–9.01 | 663,255 | 45.12 | 12 | 19.35 | 0.43 | |
9.01–38.03 | 341,553 | 23.24 | 9 | 14.52 | 0.62 | |
>38.03 | 83,751 | 5.70 | 10 | 16.13 | 2.83 | |
Distance to stream (m) | <100 | 408,797 | 27.67 | 29 | 46.77 | 1.69 |
100–200 | 298,966 | 20.24 | 16 | 25.81 | 1.28 | |
200–300 | 245,504 | 16.62 | 9 | 14.52 | 0.87 | |
300–400 | 157,383 | 10.65 | 6 | 9.68 | 0.91 | |
>400 | 366,502 | 24.81 | 2 | 3.23 | 0.13 | |
Distance to road (m) | <500 | 224,545 | 15.20 | 21 | 33.87 | 2.23 |
500–1000 | 193,768 | 13.12 | 15 | 24.19 | 1.84 | |
1000–1500 | 166,957 | 11.30 | 13 | 20.97 | 1.86 | |
1500–2000 | 147,431 | 9.98 | 9 | 14.52 | 1.45 | |
>2000 | 744,451 | 50.40 | 4 | 6.45 | 0.13 | |
Distance to fault (m) | <500 | 102,353 | 6.93 | 0 | 0.00 | 0.00 |
500–1000 | 103,021 | 6.97 | 0 | 0.00 | 0.00 | |
1000–1500 | 101,830 | 6.89 | 0 | 0.00 | 0.00 | |
1500–2000 | 101,938 | 6.90 | 0 | 0.00 | 0.00 | |
>2000 | 103,846 | 7.03 | 3 | 4.84 | 0.69 | |
Drainage density (km/km2) | <0.85 | 398,195 | 26.96 | 0 | 0.00 | 0.00 |
0.85–1.49 | 497,684 | 33.69 | 4 | 6.45 | 0.19 | |
1.49–2.19 | 368,973 | 24.98 | 34 | 54.84 | 2.20 | |
>2.19 | 212,300 | 14.37 | 24 | 38.71 | 2.69 | |
Rainfall | <248.5 | 284,466 | 19.26 | 52 | 83.87 | 4.36 |
248.5–350.3 | 535,319 | 36.24 | 10 | 16.13 | 0.45 | |
350.3–461.02 | 321,491 | 21.76 | 0 | 0.00 | 0.00 | |
461.02–582.7 | 208,018 | 14.08 | 0 | 0.00 | 0.00 | |
>582.7 | 127,857 | 8.66 | 0 | 0.00 | 0.00 | |
Soil type | Entisols | 486,282 | 32.92 | 2 | 3.23 | 0.10 |
Alfisols | 7086 | 0.48 | 0 | 0.00 | 0.00 | |
Aridisols | 161,526 | 10.93 | 50 | 80.65 | 7.37 | |
Entisols/Inceptisols | 479,698 | 32.47 | 10 | 16.13 | 0.50 | |
Inceptisols | 24,777 | 1.68 | 0 | 0.00 | 0.00 | |
Mollisols | 317,783 | 21.51 | 0 | 0.00 | 0.00 | |
LU/LC | Agriculture | 281,159 | 19.03 | 62 | 100.00 | 5.25 |
Dense forest | 155 | 0.01 | 0 | 0.00 | 0.00 | |
Good range | 8660 | 0.59 | 0 | 0.00 | 0.00 | |
Agri-dry farming | 243 | 0.02 | 0 | 0.00 | 0.00 | |
Dry farming | 16,235 | 1.10 | 0 | 0.00 | 0.00 | |
Low forest | 337,471 | 22.85 | 0 | 0.00 | 0.00 | |
Woodland | 233,619 | 15.82 | 0 | 0.00 | 0.00 | |
Mod-forest | 83,527 | 5.65 | 0 | 0.00 | 0.00 | |
Mod-range | 105,592 | 7.15 | 0 | 0.00 | 0.00 | |
Poor-range | 382,114 | 25.87 | 0 | 0.00 | 0.00 | |
Rock | 23,154 | 1.57 | 0 | 0.00 | 0.00 | |
Urban | 5220 | 0.35 | 0 | 0.00 | 0.00 | |
Lithology | A | 76,475 | 5.18 | 0 | 0.00 | 0.00 |
B | 131,673 | 8.91 | 0 | 0.00 | 0.00 | |
C | 114,748 | 7.77 | 5 | 8.06 | 1.04 | |
D | 94,149 | 6.37 | 0 | 0.00 | 0.00 | |
E | 33,722 | 2.28 | 0 | 0.00 | 0.00 | |
F | 117,907 | 7.98 | 0 | 0.00 | 0.00 | |
G | 31,564 | 2.14 | 0 | 0.00 | 0.00 | |
H | 134,059 | 9.08 | 0 | 0.00 | 0.00 | |
I | 594,531 | 40.25 | 57 | 91.94 | 2.28 | |
J | 148,324 | 10.04 | 0 | 0.00 | 0.00 | |
NDVI | <−0.01 | 18,508 | 1.26 | 18 | 29.03 | 23.12 |
−0.01–0.07 | 44,145 | 2.99 | 15 | 24.19 | 8.08 | |
0.07–0.12 | 89,947 | 6.10 | 13 | 20.97 | 3.44 | |
0.12–0.21 | 362,491 | 24.59 | 10 | 16.13 | 0.66 | |
0.21–0.32 | 538,553 | 36.53 | 6 | 9.68 | 0.26 | |
>0.32 | 420,479 | 28.52 | 0 | 0.00 | 0.00 |
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Arabameri, A.; Lee, S.; Tiefenbacher, J.P.; Ngo, P.T.T. Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran). Remote Sens. 2020, 12, 490. https://doi.org/10.3390/rs12030490
Arabameri A, Lee S, Tiefenbacher JP, Ngo PTT. Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran). Remote Sensing. 2020; 12(3):490. https://doi.org/10.3390/rs12030490
Chicago/Turabian StyleArabameri, Alireza, Saro Lee, John P. Tiefenbacher, and Phuong Thao Thi Ngo. 2020. "Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)" Remote Sensing 12, no. 3: 490. https://doi.org/10.3390/rs12030490
APA StyleArabameri, A., Lee, S., Tiefenbacher, J. P., & Ngo, P. T. T. (2020). Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran). Remote Sensing, 12(3), 490. https://doi.org/10.3390/rs12030490