# An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}with a population of 1,332,912 [56]. The Abbottabad district is situated to the south-west of Muzaffarabad district where the epicenter of the devastating Kashmir 2005 earthquake is located. The maximum elevation in the region is 2957 m above sea level. This region consists of fragile geology of igneous, metamorphic, and sedimentary rocks. According to Gansser et al., 1964 [57] classification of tectonostratigraphic zones, the study area is a part of the lesser Himalayan fold and thrust belt, enclosed to the south by main boundary thrust (MBT) and to the north by main mantle thrust (MMT) [58]. Panjal thrust, Nathia Gali thrust fault, Gandghar fault, Kuzagali fault, and MBT run across the Abbottabad district. Panjal thrust fault trends in a northeast-southwest direction with a dip facing south-east in most northern regions and a north-west dip facing the south-western. The Nathia Gali thrust fault is oriented roughly towards the southwest with a northwest dip direction.

#### 2.2. Methodological Framework

#### 2.3. Landslide Inventory Dataset

#### 2.4. Landslide Conditioning Factors Dataset

#### 2.5. Susceptibility Modeling Techniques

#### 2.5.1. Linear Regression

_{0}is an intercept and has a fixed value in the regression equation; β

_{1}to β

_{n}are coefficients (weight); X

_{1}to X

_{n}represent the independent variables, and ε represents the model error term.

#### 2.5.2. Logistic Regression

_{0}is the intercept of a mode; b

_{1}… b

_{n}are the coefficients of the LoR model, and x

_{1}… x

_{n}represent the independent variables.

#### 2.5.3. Support Vector Machine

#### 2.6. Model Evaluation and Accuracy Assessment

## 3. Results

#### 3.1. Thematic Maps of Conditioning Factors

#### 3.2. Conditioning Factor Analysis

#### 3.3. Landslide Susceptibility (LS) Maps

#### 3.3.1. Linear Regression (LiR)

#### 3.3.2. Logistic Regression (LoR)

#### 3.3.3. Support Vector Machine

#### 3.4. Model Validation

#### 3.5. Accuracy Assessment

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Location map of the study area showing Abbottabad district along with the distribution of faults and inventory of 116 landslides derived from Landsat 8 pre and post event imageries.

**Figure 2.**Methodology flowchart used in the preparation of susceptibility map (NDWI is Normalized Difference Water Index, LCCS is Landcover Classification System, TRI is Topographic Roughness Index, FAO is Food and Agricultural Organization, and NDVI is Normalized Difference Vegetation Index).

**Figure 3.**Google Earth Pro 7.3 is used to generate (

**A**,

**B**). Pre landslide imagery (2014) (

**A**), and post landslide imagery (

**B**). Picture (

**C**,

**D**) Illustrate field imagery of the Poona landslide, Havelian in the study area.

**Figure 4.**Historical landslide and generated non-landslide points were used for testing and training in the study.

**Figure 5.**Landslide conditioning factor maps used in this study: (

**a**) LCCS, (

**b**) soil type (from bedrock erosion), (

**c**) NDWI, (

**d**) slope, (

**e**) lithology, (

**f**) NDVI, (

**g**) elevation, (

**h**) fault density, (

**i**) road density, (

**j**) profile curvature, (

**k**) plan curvature, (

**l**) total curvature, (

**m**) Aspect, (

**n**) TRI.

**Figure 10.**A histogram shows susceptible areas from different models that fall into various classes.

Dataset | LoR | SVM | LiR |
---|---|---|---|

Aspect | 6 | 9 | 5 |

Curvature | 9 | 6 | 9 |

Elevation | 8 | 7 | 8 |

Lithology | 9 | 10 | 9 |

NDVI | 6 | 8 | 8 |

NDWI | 9 | 10 | 8 |

TRI | 7 | 6 | 8 |

Plane Curvature | 4 | 5 | 5 |

Profile Curvature | 5 | 3 | 4 |

Slope | 9 | 8 | 9 |

Faults | 7 | 6 | 6 |

Roads | 5 | 5 | 5 |

Soil | 7 | 8 | 9 |

LCCS | 9 | 9 | 7 |

Total | 100 | 100 | 100 |

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**MDPI and ACS Style**

Ullah, I.; Aslam, B.; Shah, S.H.I.A.; Tariq, A.; Qin, S.; Majeed, M.; Havenith, H.-B.
An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping. *Land* **2022**, *11*, 1265.
https://doi.org/10.3390/land11081265

**AMA Style**

Ullah I, Aslam B, Shah SHIA, Tariq A, Qin S, Majeed M, Havenith H-B.
An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping. *Land*. 2022; 11(8):1265.
https://doi.org/10.3390/land11081265

**Chicago/Turabian Style**

Ullah, Israr, Bilal Aslam, Syed Hassan Iqbal Ahmad Shah, Aqil Tariq, Shujing Qin, Muhammad Majeed, and Hans-Balder Havenith.
2022. "An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping" *Land* 11, no. 8: 1265.
https://doi.org/10.3390/land11081265