# Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}with an average elevation of 1700 m. A semiarid climate dominates the Hesare-No area and severe water scarcity has been a problem during the past decades. Further, the minimum and maximum temperatures are 4 °C and 40 °C, respectively, and the average annual precipitation is about 249 mm. Although the annual rainfall has continuously decreased, most likely due to climate change, farming activities have increased, leading to an increased water shortage in the region. The main source of water is GW since surface water resources are not adequate to meet the water demands for agricultural, domestic, and industrial targets. The Hesare-No is a mountainous area with 620 springs. This study split the spring data into 70%, i.e., 434 cases, and 30%, i.e., 186 cases, for training and validating the MLAs, respectively. Moreover, to construct the MLAs, we randomly generated 620 non-spring or absence data.

#### 2.2. GW Spring Driving Factors

#### 2.2.1. Topographical Driving Factors

#### Altitude

#### Slope Degree

#### Aspect

#### Slope Length (LS)

_{s}is the specific basin area (m

^{2}) and α is the slope gradient in degrees. The LS of the Hesare-No area ranges between 0 and 53 (m) (Figure 3d).

#### Plan and Profile Curvatures

#### Relative Slope Position (RSP)

#### 2.2.2. Hydrological Driving Factors

#### Distance from Rivers and River Density (Rd)

^{2}(Figure 4b).

#### Topographic Wetness Index (TWI)

#### 2.2.3. RS-Derived Factors

#### Satellite Data and Pre-Processing

#### Generation of Land Use/Land-Cover Classification and Accuracy Assessment

#### Retrieval of Normalized Difference Vegetation Index (NDVI)

#### Distance from Lineament and Lineament Density

#### 2.2.4. Lithology

#### 2.3. Machine Learning Algorithms

#### 2.3.1. Logistic Model Tree

_{i}, representing the driving factors. To apply the logistic model tree, R statistical software (R Foundation for Statistical Computing, Vienna, Austria) and two packages including “caret” [64] and “RWeka” [65] were used through a 10-fold cross-validation.

#### 2.3.2. Deep Boosting

#### 2.3.3. Boosted Regression Trees

#### 2.3.4. K-Nearest Neighbors

#### 2.3.5. Random Forest

#### 2.4. Validation of the Algorithms

## 3. Results

#### 3.1. Machine Learning Algorithm Parameter Optimization Results

#### 3.2. Validation of Maps and Performance Analysis of the Algorithms

#### 3.3. GW Potential Maps

^{2}of the Hesare-No Basin are classified as very high GW potential by the k-nearest neighbors, logistic model tree, boosted regression trees, random forest, and deep boosting, respectively. On the other hand, low GW potential was predicted to cover 371.02, 342.25, 315.04, 230.23, and 220.90 km

^{2}of the total basin area by the logistic model tree, boosted regression trees, random forest, deep boosting, and k-nearest neighbors, respectively. The percentage of each class by the logistic model tree, deep boosting, boosted regression trees, k-nearest neighbors, and random forest is shown in Figure 10. It can be observed that 29%, 28%, 27%, 27%, and 24% of the Hesare-No Basin are classified as high and very high GW potential by the k-nearest neighbors, deep boosting, random forest, boosted regression trees, and logistic model tree, respectively. Additionally, it was demonstrated that the highest percentage of “low, moderate, high, and very high” classes belonged to the logistic model tree, k-nearest neighbors, deep boosting, and k-nearest neighbors MLAs, respectively.

#### 3.4. Importance of Factors

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 3.**Topographical driving factors of Hesare-No Basin including: (

**a**) altitude, (

**b**) slope degree, (

**c**) aspect, (

**d**) slope length, (

**e**) plan curvature, (

**f**) profile curvature, and (

**g**) relative slope position.

**Figure 4.**Hydrological driving factors of the Hesare-No Basin including (

**a**) distance from rivers, (

**b**) river density, and (

**c**) topographic wetness index.

**Figure 5.**Remote sensing (RS)-derived factors including: (

**a**) LULC, (

**b**) Normalized difference vegetation index (NDVI), (

**c**) distance from lineament, and (

**d**) lineament density.

**Figure 6.**Lithology of the study area (symbols are defined in Table 2).

**Figure 9.**Distribution of groundwater (GW) potential in the study area based on: (

**a**) logistic model tree, (

**b**) deep boosting, (

**c**) boosted regression trees, (

**d**) k-nearest neighbors, and (

**e**) random forest MLAs.

**Figure 10.**Percentage of each class of the GW potential maps constructed by the logistic model tree, deep boosting, boosted regression trees, and k-nearest neighbors.

Indices | Classification Algorithm | ||
---|---|---|---|

Maximum Likelihood | Neural Network | Decision Tree | |

Overall Accuracy (%) | 87 | 88 | 91 |

Kappa Coefficient (%) | 76 | 78 | 82 |

Geology Group | Description | Age |
---|---|---|

Jmz | Grey thick-bedded limestone and dolomite (Mozduran formation) | Middle-Late Jurassic |

Jd | Well-bedded to thin-bedded, greenish-grey argillaceous limestone with intercalations of calcareous shale (Dalichai formation) | Jurassic |

PlQc | Fluvial conglomerate, Piedmont conglomerate, and sandstone | Pliocene-Quaternary |

Jl | Light grey, thin-bedded to massive limestone (Lar formation) | Jurassic-Cretaceous |

Qft2 | Low level piedmont fan and valley terrace deposits | Quaternary |

Ea.bvt | Andesitic to basaltic volcanic tuff | Eocene |

PlQdv | Rhyolitic to Rhyodacitic volcanics | Pliocene-Quaternary |

Jph | Phyllite, slate, and meta-sandstone (Hamadan Phyllites) | Jurassic |

E3c | Conglomerate and sandstone | Eocene |

E2sht | Tuffaceous shale and tuff | Eocene |

E2m | Pale red marl, gypsiferous marl, and limestone | Eocene |

Mur | Red marl, gypsiferous marl, sandstone, and conglomerate (Upper Red formation) | Miocene |

Pz | Undifferentiated lower Paleozoic rocks | Early Palaeozoic |

Osh | Greenish-grey siltstone and shale with intercalations of flaggy limestone (Shirgesht formation) | Ordovician |

Eav | Andesitic volcanics | Middle Eocene |

Indices | Logistic Model Tree | Deep Boosting | Boosted Regression Trees | K-Nearest Neighbors | Random Forest |
---|---|---|---|---|---|

Accuracy | 0.8387 | 0.8118 | 0.8065 | 0.7581 | 0.8010 |

Kappa | 0.6774 | 0.6237 | 0.6129 | 0.5161 | 0.6022 |

ROC (%) | 87.813 | 87.807 | 87.397 | 76.708 | 86.466 |

Sensitivity | 0.7849 | 0.7527 | 0.7957 | 0.7742 | 0.7750 |

Specificity | 0.8925 | 0.8710 | 0.8172 | 0.7419 | 0.8270 |

Mean Rank | p-Value (α = 0.05) | χ^{2}(Chi-Square) | ||||
---|---|---|---|---|---|---|

Logistic Model Tree | Deep Boosting | Boosted Regression Trees | K-Nearest Neighbors | Random Forest | ||

4.80 | 3.40 | 3.20 | 1.20 | 2.40 | 0.007 | 14.08 |

**Table 5.**Range and area of each class of the GW potential maps constructed by the logistic model tree, deep boosting, boosted regression trees, and k-nearest neighbors.

Model/Class | Low | Moderate | High | Very High | |
---|---|---|---|---|---|

Logistic model tree | Range | 0–0.11 | 0.11–0.37 | 0.37–0.70 | 0.70–1 |

Area (km^{2}) | 371.02 | 169 | 79.22 | 92.9 | |

Deep boosting | Range | 0.02–0.27 | 0.27–0.43 | 0.43–0.60 | 0.60–0.98 |

Area (km^{2}) | 230.23 | 277.32 | 141.73 | 62.83 | |

Boosted regression trees | Range | 0.09–0.21 | 0.21–0.39 | 0.39–0.62 | 0.62–0.89 |

Area (km^{2}) | 342.25 | 172.48 | 109.87 | 87.44 | |

K-nearest neighbors | Range | 0–0.04 | 0.04–0.42 | 0.42–0.71 | 0.71–1 |

Area (km^{2}) | 220.9 | 280.68 | 99.8 | 110.77 | |

Random forest | Range | 0–0.16 | 0.16–0.36 | 0.36–0.61 | 0.61–1 |

Area (km^{2}) | 315.06 | 198.71 | 124.42 | 73.93 |

Factor | Boosted Regression Trees (Relative Influence) | K-Nearest Neighbors | Random Forest(Mean Decrease Gini) | Logistic Model Tree |
---|---|---|---|---|

NDVI | 100 | 100 | 41.973 | 100 |

Distance from rivers | 16.03 | 46.073 | 16.57 | 46.07 |

Altitude | 20.73 | 43.473 | 19.492 | 43.473 |

RSP | 17.47 | 37.202 | 18.109 | 37.202 |

Profile curvature | 5.25 | 33.501 | 10.12 | 33.501 |

Distance from lineament | 2.558 | 33.424 | 10.77 | 33.424 |

Lineament density | 0 | 32.108 | 8.862 | 32.108 |

Land use-cover | 18.26 | 17.034 | 17.02 | 29.146 |

Plan curvature | 1.038 | 26.539 | 9.017 | 26.539 |

TWI | 3.17 | 23.34 | 9.923 | 23.34 |

River density | 1.288 | 17.602 | 7.263 | 17.602 |

Lithology | 4.949 | 7.749 | 8.758 | 10.371 |

Slope length | 0.105 | 8.372 | 6.423 | 8.372 |

Slope degree | 1.835 | 4.051 | 6.623 | 4.051 |

Aspect | 0.285 | 0 | 3.002 | 0 |

© 2020 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**

Kamali Maskooni, E.; Naghibi, S.A.; Hashemi, H.; Berndtsson, R. Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data. *Remote Sens.* **2020**, *12*, 2742.
https://doi.org/10.3390/rs12172742

**AMA Style**

Kamali Maskooni E, Naghibi SA, Hashemi H, Berndtsson R. Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data. *Remote Sensing*. 2020; 12(17):2742.
https://doi.org/10.3390/rs12172742

**Chicago/Turabian Style**

Kamali Maskooni, Ehsan, Seyed Amir Naghibi, Hossein Hashemi, and Ronny Berndtsson. 2020. "Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data" *Remote Sensing* 12, no. 17: 2742.
https://doi.org/10.3390/rs12172742