# A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping

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## Abstract

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## 1. Introduction

^{3}, while the recharge of groundwater globally has reached 12,700 km

^{3}/year [5]. Furthermore, the level of pollution and wider distribution of groundwater is low, which, in turn, has attracted more human population throughout the world [6].

## 2. Research Area and Groundwater Spring Geodatabase

#### Description of Research Area

^{2}. The elevation of the study area ranges from 1550 to 2859 m (Figure 1). The Ghishlagh Dam is located at the outlet of the Chilgazi watershed. The average annual temperature is 14.2 °C; the average daily minimum temperature in winter is 6.5 °C, and the average daily maximum temperature in summer is about 37 °C. The average annual precipitation is 464.2 mm, such that it mainly occurs in December to April (more than 75%). The climate of the study area based on De-Marttone climatic system is classified as semi-arid [109]. Most of the area is covered by agricultural lands (23,465 ha) and rangelands (3768 ha). In addition, barren lands, pastures, residential areas, and gardens are other types of land use in the study area. The study area is geologically part of elevated Zagros (Northern Zagros) where joints, gaps, and faults have been created. Also, soil of the study area is mainly semi-deep with predominant sandy-loamy texture. Most of the study area has been covered by the Quaternary deposits, including andesite–basalt (${\mathrm{K}}_{\mathrm{v}\mathrm{c}}$), Sanandaj shale (${\mathrm{K}}_{\mathrm{S}}^{\mathrm{S}}$), and limestone (${\mathrm{K}}_{\mathrm{u}}^{\mathrm{l}}$). In addition, surface water and groundwater are the two sources of water supply, where surface water is often used for irrigation purposes and groundwater is commonly utilized for agricultural production as well as domestic purposes.

## 3. Data Acquisition

#### 3.1. Data Collection and Interpretation

#### 3.2. Groundwater Spring Conditioning Factors

#### 3.2.1. Topographic Factors

**Slope angle**was considered as a terrain feature to recognize groundwater conditions [112,113]. It affects the recharge through infiltration so that the more the slope angle is, the greater the infiltration and the recharge are [114]. Slope angle of the study area ranged from 10 to >40 degrees which was then classified into five classes, such as (1) 0–10; (2) 10–20; (3) 20–30; (4) 30–40; and (5) >40 (Table 2).

**Slope aspect**is a well-known conditioning factor of GSPM [12,13,22]. Slope aspect can affect hydrologic response, such as solar radiation, soil-water retention, soil porosity, hydraulic conductivity, snow ablation, evapotranspiration, water cycling, and vegetation communities [115,116,117,118,119]. Generally, in the northern hemisphere, north-facing slopes are colder and wetter than south-facing slopes, which are warmer and drier [120]. Therefore, the north-facing slopes have more potential for spring occurrence that indicates that groundwater is higher than at the other places. The slope aspect map of this area was derived from DEM with nine classes (Table 2), including Flat, North, Northeast, East, Southeast, South, Southeast, West, and Northwest.

**Elevation**is known as the height above the earth surface; it is related with climate and environment, thus affecting groundwater springs [121]. It can affect the weather and climate change, and can influence soil properties and vegetation communities [39]. Basically, the higher the elevation is, the more the potential of springs because of more rainfall in comparison to lower elevations. The elevation map of this study was extracted from DEM and classified into five classes, including (1) <1800; (2) 1800–1900; (3) 1900–2000; (4) 2000–2200; and (5) >2200 (Table 2).

**Curvature**generally has a negative relationship with groundwater recharge [13]. Thus, it is considered as a conditioning factor affecting groundwater spring [13]. The curvature map of the study area was generated in five categories: (1) ((−13.5)–(−2.24)); (2) ((−2.24)–(−0.661)); (3) ((−0.661)–(−0.394)); (4) >((−0.394)–(−1.66)); and (5) ((−1.66)–(−13.3)) (Table 2).

**Plan curvature**and

**profile curvature**are the curvatures of a contour line formed by intersecting the surface with a horizontal plan and a vertical plan, respectively; thus, they affect groundwater springs [83]. Plan curvature describes the divergence and convergence of flow and it can affect the concentration of flow on the ground [122]. However, profile curvature can affect the pore water pressure, saturated and recharge resulting in the development of groundwater. The plan curvature map of study area was extracted from DEM and classified into five levels, such as (1) ((−7.78)–(−1.3)); (2) ((−1.3)–(−0.381)); (3) ((−0.381)–0.339); (4) >(0.339–1.45); and (5) (1.45–8.91) (Table 2). The profile curvature was also extracted from DEM and classified into five classes, including (1) ((−7.13)–(−1.46)); (2) ((−1.46)–(-0.450)); (3) ((−0.450)–0.141); (4) >(0.141–0.791); and (5) (0.791–7.89) (Table 2).

**STI**/low susceptibility (

**LS)**, as an important conditioning factor in the study of groundwater spring, shows the erosion power of overland streams due to two structural elements, including carrier content of alluvium flow and basin evolvement [123,124]. The STI is computed from the following equation:

^{2}/m), and $\mathsf{\beta}$ the slope gradient [78]. In this study, the STI values of study area were divided into five classes, involving (1) 0–3.83; (2) 3.83–8.66; (3) 8.66–13.3; (4) 13.3–18.8; and (5) 18.8–42.5 (Table 2).

#### 3.2.2. Hydrological Factors

**Rainfall**is a hydrologic process for recharging aquifers [125,126]. Groundwater potentiality increases as rainfall increases [114]. In this study, the mean annual rainfall data of ten meteorological stations were acquired from the I.R. of Iran Meteorological Organization (IRIMO). Rainfall in the study area ranged between 300 and >480 mm, which was then classified into seven classes, including (1) 300–340; (2) 340–360; (3) 360–380; (4) 380–400; (5) 400–440; (6) 440–480; and (7) >480 (Table 2).

**SPI**has been considered as one of the conditioning factors which contributes to groundwater springs [41,42]. Generally, the higher the SPI is, the higher the potential for spring occurrence because of having a higher water table. It was extracted DEM, where SPI values can be computed by the following equation [127]:

**TWI**is an important conditioning factor for GSPM [13,128], as permeability and pore water pressure of materials are affected by water infiltration and soil strength [42]. It has been extensively used to describe the effect of topography on the size and location of saturated source areas which are prone to runoff generation. Basically, areas with higher TWI indicate also the higher potential for spring occurrence. The following equation was used for the TWI computation [129]:

**Distance to rivers**affects the moisture content of soil and rock on the slope, thus affecting groundwater springs [28]. This factor can affect the recharge process so that the shorter the distance from river, the higher the potential to infiltration in comparison to farther distance from the river networks [130]. According to the DEM of the study area, the multi-buffer values of rivers were generated with five classes: (1) 0–100; (2) 100–200; (3) 200–300; (4) 300–400; and (5) >400 (Table 2).

**River density**is considered as an important conditioning factor for GSPM [114], as when the drainage density is lower, the infiltration and recharge are greater [125,131]. The higher the drainage density is, the lower the infiltration and the higher the surface runoff are, which indicates that this factor has a reverse relationship with groundwater [131]. The river density of the study area varied from 0 to 0.00633 (km/km

^{2}) which was then divided into five categories: (1) 0–0.000744; (2) 0.000744–0.00169; (3) 0.00169–0. 00248; (4) 0. 00248–0.00337; and (5) 0.00337–0.00633 (Table 2).

#### 3.2.3. Geological Factors

**Lithology**is related to both soil porosity and water permeability of aquifers [114,132]. In general, karst and fissured rock aquifers have lower capacity and specific storage of groundwater springs than sedimentary aquifers [114]. The lithology map of the area was constructed from the geological map at 1:100,000 scale collected from the Geological Survey & Mineral Exploitation of Iran (GSMEI). In this study, lithology was reclassified into ten classes, including (1) alluvial fan and terraces (Qt1 and Qt2); (2) alluvial deposits (Qal); (3) limestone (Kul, Kpf, and Kf1); (4) sandstone (Kvsl); (5) shale (Kss); (6) turbidite sequence (Ktsc); (7) conglomerate with intermediate of Sandston (Klt); (8) un-granulated conglomerate with shale and sandstone (Kco); (9) lava and tuff (Kvc and Kv); and (10) coarse-grained gabbro (gb) (Table 2).

**Distance to fault**is another vital factor for studying groundwater springs. This factor can affect infiltration so that the shorter the distance from the fault, the higher the potential to infiltration in comparison to farther distance from the river networks. Different types of faults can control the movement of groundwater springs on the geological structure of an area [15]. Faults of the study area were extracted from the geological map at 1:100,000 scale and distance to faults map was constructed with five categories, such as (1) 0–100; (2) 100–200; (3) 200–300; (4) 300–400; and (5) >400 (Table 2).

**Fault density**is described as the relationship between the sum of fault lengths in the pixel and the area of the corresponding pixel [121]. The areas with more faults, if they receive enough moisture and water, are also more likely to develop springs and develop aquifers than the areas with less faults. Therefore, these areas easily recharge the groundwater aquifers. The fault density of the study area was calculated from the geological map at 1:100,000 scale and was then divided into five classes: (1) 0–0.000418; (2) 0.000418–0.00114; (3) 0.00114–0.00185; (4) 0.00185–0.00267; and (5) 0.00267–0.00508 km/km

^{2}(Table 2).

**Permeability**is one of the geological factors that affects the groundwater spring occurrence using discontinuity structures, such as joints, cracks, and faults. This factor was evaluated using expert knowledge and field surveys based on the lithological units. Eventually, the permeability map was classified into four categories, including very low, low, moderate, and high (Table 2).

#### 3.2.4. Land Cover Factors

**Land use**affects infiltration and runoff, thus affecting GSPM [131]. Moreover, the development of groundwater spring resources is due to land use [133]. In this study, land use was generated from ETM

^{+}satellite images in 2013 with different classes: (1) Woodland; (2) residential area; (3) barren land; (4) outcrop land; (5) range land; (6) dry farming land; and (7) farming land (Table 2).

## 4. Theoretical Background of Machine Learning Algorithms

#### 4.1. Logistic Regression (LR)

#### 4.2. Logistic Model Tree (LMT)

#### 4.3. Stochastic Gradient Descent (SGD)

#### 4.4. Support Vector Machine (SVM)

#### 4.5. Alternating Decision Tree (ADTree)

#### 4.6. Random Forest (RF)

#### 4.7. AB Learning Ensemble Techniques

#### “AB–ADTree” Model

#### 4.8. Accuracy Assessment (Validation) and Comparison of Methods

#### 4.8.1. Statistical Measures

#### 4.8.2. Receiver Operating Characteristics Curve (ROC)

#### 4.8.3. Statistical Assessment

#### 4.9. Selection of Training Factors Using Chi-Square Technique

## 5. Results and Analysis

#### 5.1. Groundwater Spring Conditioning Factor Analysis

#### 5.2. Model Training and Assessment

#### 5.3. Models Validation and Comparison

#### 5.4. Groundwater Spring Potential Mapping

#### 5.5. GSPM Validation and Comparison

#### 5.6. Similarities Between Prediction Power of Models

## 6. Discussion

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 2.**The flowchart of the methodology for groundwater spring potential mapping. Abbreviations: ROC, receiver operating characteristic.

**Figure 5.**Area under the ROC curve (AUROC) of the seven models for groundwater potential mapping (GSPM) using training (

**a**) and validation (

**b**) datasets.

Minimum | Maximum | Mean | SD | Variance | |
---|---|---|---|---|---|

Q (lit/s) | 0.2 | 10 | 0.5631 | 0.278 | 0.278 |

T (°C) | 0.3 | 27 | 14.812 | 4.636 | 21.494 |

EC (µmho/cm) | 0.0 | 627 | 364.688 | 126.576 | 160.216 |

pH | 0.1 | 8.8 | 7.525 | 2.097 | 4.398 |

**Table 2.**Groundwater spring conditioning factors and their classifications for modeling groundwater spring potential mapping (GSPM) at Chilgazi watershed. Abbreviations: SPI, sediment transport power; TWI, topographic wetness index; LS, low susceptibility; STI, sediment transport index.

Main Factors | No. | Conditioning Factors | Classes |
---|---|---|---|

Topographic | 1 | Slope (^{o}) | (1) 0–10; (2) 10–20; (3) 20–30; (4) 30–40; and (5) >40 |

2 | Aspect | (1) Flat; (2) North; (3) Northeast; (4) East; (5) Southeast; (6) South; (7) Southwest; (8) West; and (9) Northwest | |

3 | Elevation (m) | (1) <1800; (2) 1800–1900; (3) 1900–2000; (4) 2000–2200; and (5) >2200 | |

4 | Curvature | (1) ((−13.5)–(−2.24)); (2) ((−2.24)–(−0.661)); (3) ((−0.661)–(−0.394)); (4) >((−0.394)–(−1.66)); and (5) ((−1.66)–(−13.3)) | |

5 | Plan curvature | (1) ((−7.78)–(−1.3)); (2) ((−1.3)–(−0.381)); (3) ((−0.381)–0.339); (4) >(0.339–1.45)); and (5) (1.45–8.91)) | |

6 | Profile curvature | (1) ((−7.13)–(−1.46)); (2) ((−1.46)–(−0.450)); (3) ((−0.450)–0.141); (4) >(0.141–0.791)); and (5) (0.791–7.89)) | |

7 | SPI | (1) 0–500; (2) 500–1000; (3) 1000–1500; (4) 1500–2000; and (5) 2000–116000 | |

Hydrological | 8 | TWI | (1) 0.649–3.31; (2) 3.31–4.16; (3) 4.16–6.42; (4) 6.42–8.88; and (5) 8.88–10.9 |

9 | STI/LS | (1) 0–3.83; (2) 3.83–8.66; (3) 8.66–13.3; (4) 13.3–18.8; and (5) 18.8–42.5 | |

10 | Rainfall (mm/y) | (1) 300–340; (2) 340–360; (3) 360–380; (4) 380–400; (5) 400–440; (6) 440–480; and (7) >480 | |

11 | Distance to rivers (m) | (1) 0–100; (2) 100–200; (3) 200–300; (4) 300–400; and (5) >400 | |

12 | River density (km/km^{2}) | (1) 0–0.000744; (2) 0.000744–0.00169; (3) 0.00169–0. 00248; (4) 0. 00248–0.00337; and (5) 0.00337–0.00633 | |

Geological | 13 | Lithology | (1) Alluvial fan and terraces (Qt1 and Qt2); (2) alluvial deposits (Qal); (3) limestone (Kul, Kpf and Kf1); (4) sandstone (Kvsl); (5) shale (Kss); (6) turbidite sequence (Ktsc); (7) conglomerate with intermediate of Sandston (Klt); (8) un-granulated conglomerate with shale and sandstone (Kco); (9) lava and tuff (Kvc and Kv); and (10) coarse-grained gabbro (gb) |

14 | Distance to faults (m) | (1) 0–100; (2) 100–200; (3) 200–300; (4) 300–400; and (5) >400 | |

15 | Fault density(km/km^{2}) | (1) 0–0.000418; (2) 0.000418–0.00114; (3) 0.00114–0. 00185; (4) 0. 00185–0.00267; and (5) 0.00267–0.00508 | |

16 | Permeability | (1) Very low; (2) low; (3) moderate; and (4) high | |

Land cover | 17 | Land use | (1) Woodland; (2) residential area; (3) barren land; (4) outcrop land; (5) range land; (6) dry farming land; and (7) farming land |

**Table 3.**Confusion matrix. Abbreviations: TP, number of pixels correctly classified as positive (springs) predictions; TN, number of pixels correctly classified as negative (non-springs) predictions; FP, number of pixels incorrectly classified as positive (springs) predictions; FN, number of pixels incorrectly classified as negative (non-springs) predictions.

Predicted | ||||
---|---|---|---|---|

${X}_{1}^{\prime}$ (Spring) | ${X}_{0}^{\prime}$ (Non-Spring) | Sum | ||

Observed | ${X}_{1}^{\prime}$ (spring) | TP | FN | P |

${X}_{0}^{\prime}$ (non-spring) | FP | TN | N |

**Table 4.**GSPM model validation using training dataset. Abbreviations: LMT, logistic model tree; LR, logistic regression; SVM, support vector machine; RF, random forest; SGD, stochastic gradient descent; PPV, positive predictive value; NPV, negative predictive value; RMSE, root mean square error; AUC, area under the curve.

AB–ADTree | ADTree | SGD | LMT | LR | SVM | RF | |
---|---|---|---|---|---|---|---|

True positive | 344 | 310 | 336 | 324 | 326 | 327 | 332 |

True negative | 366 | 341 | 316 | 335 | 333 | 332 | 333 |

False positive | 78 | 103 | 128 | 109 | 111 | 112 | 111 |

False negative | 100 | 134 | 108 | 120 | 118 | 117 | 112 |

PPV (%) | 0.815 | 0.751 | 0.724 | 0.748 | 0.746 | 0.745 | 0.749 |

NPV (%) | 0.785 | 0.718 | 0.745 | 0.736 | 0.738 | 0.739 | 0.748 |

Sensitivity (%) | 0.775 | 0.698 | 0.757 | 0.730 | 0.734 | 0.736 | 0.748 |

Specificity (%) | 0.824 | 0.768 | 0.712 | 0.755 | 0.750 | 0.748 | 0.750 |

Accuracy (%) | 0.800 | 0.733 | 0.734 | 0.742 | 0.742 | 0.742 | 0.751 |

RMSE | 0.375 | 0.424 | 0.515 | 0.418 | 0.417 | 0.418 | 0.401 |

AUC | 0.881 | 0.817 | 0.675 | 0.815 | 0.816 | 0.815 | 0.818 |

AB–ADTree | ADTree | SGD | LMT | LR | SVM | RF | |
---|---|---|---|---|---|---|---|

True positive | 145 | 143 | 150 | 146 | 147 | 147 | 147 |

True negative | 137 | 131 | 130 | 135 | 135 | 134 | 134 |

False positive | 53 | 59 | 60 | 55 | 55 | 56 | 56 |

False negative | 45 | 47 | 40 | 44 | 43 | 43 | 43 |

PPV (%) | 0.732 | 0.708 | 0.714 | 0.726 | 0.728 | 0.724 | 0.724 |

NPV (%) | 0.753 | 0.736 | 0.765 | 0.754 | 0.758 | 0.757 | 0.757 |

Sensitivity (%) | 0.763 | 0.753 | 0.789 | 0.768 | 0.774 | 0.774 | 0.774 |

Specificity (%) | 0.721 | 0.689 | 0.684 | 0.711 | 0.711 | 0.705 | 0.705 |

Accuracy (%) | 0.742 | 0.721 | 0.737 | 0.739 | 0.742 | 0.739 | 0.739 |

RMSE | 0.419 | 0.375 | 0.513 | 0.426 | 0.425 | 0.426 | 0.413 |

AUC | 0.829 | 0.790 | 0.675 | 0.803 | 0.807 | 0.805 | 0.809 |

**Table 6.**Average ranking of the seven groundwater spring potential models (GSPM) using the Friedman test.

GSPM | Mean Ranks | χ^{2} | Sig. |
---|---|---|---|

AB–ADTree | 1.34 | 3390.071 | 0.000 |

ADTree | 2.27 | ||

SGD | 5.89 | ||

LMT | 3.05 | ||

LR | 3.09 | ||

SVM | 4.90 | ||

RF | 3.59 |

**Table 7.**Performance of the seven groundwater spring potential models (GSPM) using Wilcoxon sign-rank test (two-tailed).

Pair Wise Comparison | Npd | Nnd | z-Value | p-Value | Significance |
---|---|---|---|---|---|

AB–ADTree vs. ADTree | 525 | 835 | –20.704 | 0.000 | Yes |

AB–ADTree vs. SGD | 33 | 854 | –25.320 | 0.000 | Yes |

AB–ADTree vs. LMT | 102 | 785 | –19.956 | 0.000 | Yes |

AB–ADTree vs. LR | 63 | 821 | –21.162 | 0.000 | Yes |

AB–ADTree vs. SVM | 44 | 843 | –24.197 | 0.000 | Yes |

AB–ADTree vs. RF | 59 | 836 | –13.569 | 0.010 | Yes |

ADTree vs. SGD | 0 | 887 | –25.800 | 0.000 | Yes |

ADTree vs. LMT | 361 | 526 | –6.042 | 0.000 | Yes |

ADTree vs. LR | 309 | 573 | –12.142 | 0.000 | Yes |

ADTree vs. SVM | 17 | 870 | –25.697 | 0.000 | Yes |

ADTree vs. RF | 789 | 48 | –23.658 | 0.000 | Yes |

SGD vs. LMT | 859 | 29 | –25.677 | 0.000 | Yes |

SGD vs. LR | 887 | 0 | –25.800 | 0.000 | Yes |

SGD vs. SVM | 855 | 32 | –25.558 | 0.000 | Yes |

SGD vs. RF | 815 | 26 | –22.348 | 0.020 | Yes |

LMT vs. LR | 428 | 459 | –2.067 | 0.039 | Yes |

LMT vs. SVM | 55 | 833 | –25.027 | 0.000 | Yes |

LMT vs. RF | 659 | 29 | –20.123 | 0.000 | Yes |

LR vs. SVM | 886 | 0 | –25.785 | 0.000 | Yes |

LR vs. RF | 826 | 15 | –18.236 | 0.031 | Yes |

SVM vs. RF | 802 | 18 | –19.680 | 0.000 | Yes |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Tien Bui, D.; Shirzadi, A.; Chapi, K.; Shahabi, H.; Pradhan, B.; Pham, B.T.; Singh, V.P.; Chen, W.; Khosravi, K.; Bin Ahmad, B.;
et al. A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping. *Water* **2019**, *11*, 2013.
https://doi.org/10.3390/w11102013

**AMA Style**

Tien Bui D, Shirzadi A, Chapi K, Shahabi H, Pradhan B, Pham BT, Singh VP, Chen W, Khosravi K, Bin Ahmad B,
et al. A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping. *Water*. 2019; 11(10):2013.
https://doi.org/10.3390/w11102013

**Chicago/Turabian Style**

Tien Bui, Dieu, Ataollah Shirzadi, Kamran Chapi, Himan Shahabi, Biswajeet Pradhan, Binh Thai Pham, Vijay P. Singh, Wei Chen, Khabat Khosravi, Baharin Bin Ahmad,
and et al. 2019. "A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping" *Water* 11, no. 10: 2013.
https://doi.org/10.3390/w11102013