# Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran

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## Abstract

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## 1. Introduction

## 2. Data Acquisition and Preparation

#### Study Area

^{2}and lies between the 46°10′34″ to 46°29′33″, east longitude and 35°29′7″ to 36°15′36″ north latitude (Figure 1). The Saqqez-Marivan road is a strategic and important road that connects western Iran and Iraq and facilitates trade relations with the Kurdistan region. This route is 126 km long, which includes winding and steep passages that are prone to landslides and avalanches due to heavy snow and rainfall. Figure 2 shows different types of landslides which have occurred in the study area.

## 3. Materials and Methods

#### 3.1. Landslide Conditioning Factors (LCFs)

^{+}satellite images obtained in 2017 in 12 classes (Figure 3e). A distance-to-the-fault map divided into 5 classes was made using a fault map of the research area that was created from a geological map at a scale of 1:100,000 (Figure 3f). Using the kriging method, the rainfall was mapped and then divided into five classes using 20 years of data (from 1996 to 2016) from the rain gauge stations both inside and outside of the study area (Figure 3g). The curvature is frequently employed as one of the most significant conditioning factors in landslide modelling [46]. The curvature map was produced using a DEM of 12.5 m, and it was then divided into three classes: concave, convex, and flat (no curvature) (Figure 3h). The soil layer of the region categorized into two classes was referenced from a map of Kurdistan Province showing land resources and capabilities with a scale of 1:250,000 (Figure 3i). River networks were extracted from the DEM of 12.5 m to create a distance-to-the-river map divided into five classes (Figure 3j). The excavation of roads in hilly areas causes slope instability and landslides [47]. Thu, the road network had to be obtained from the 1:50,000 scale topographic map. A distance-to-the-road map was created, divided into five classes (Figure 3k).

#### 3.2. Landslide Inventory Map (LIM)

#### 3.3. Multi-Criteria Decision-Making Methods in LSM

#### 3.3.1. Fuzzy TOPSIS Algorithm

_{j}(standard weight) is represented by Equation (2).

^{+}and A

^{−}represent options that are vastly superior and inferior, respectively [72].

#### 3.3.2. Fuzzy Analytical Network Process Model (Fuzzy ANP)

- Step A: Building ANP Models and Structuring Problems

- Step B: Pairwise comparisons

- Step C: Calculating the Super Matrix

- Step D: Selection

**Step 1:**Objective (LSM): This step involves creating the subject model and structure using 11 factors, including slope, aspect, elevation, lithology, land use, distance to fault, distance to a river, distance to road, curvature, and rainfall. These factors are classified into four clusters: topography (elevation, slope, curvature, and aspect), geology (soil type, distance to fault and lithology), anthropogenic (land use and distance to road), and climate (rainfall, distance to river).

**Step 2:**Make priority vectors and binary comparison matrices: The control criterion and experts assess the importance or priority of criteria or sub-criteria in this step, assigning a number between 1 and 9 to each. The final weights of the factors (clusters) are determined by multiplying the relative weights of the factors in the matrix from stage two. This matrix is made up of pairwise comparisons of the 11 research criteria using even comparisons and fuzzy numbers, once looking at the relationships and again at the lack of communication.

**Step 3:**Determining the criterion and sub-criterion’s ultimate weight. The final weight of each criterion is calculated in this stage by multiplying the matrices produced in the previous step. The model was run in ArcGIS after the weights of the classes of each criterion were calculated using the ANP approach and fuzzy membership functions. The weighting of criteria and sub-criteria was achieved by using the Super Decision software.

#### 3.4. Validation of the Methods

## 4. Results and Analysis

#### 4.1. Model Building and Comparison

#### 4.2. Developing Landslide Susceptibility Mapping

## 5. Discussion

## 6. Conclusions

- The three most significant factors influencing landslide occurrence were distance to the road, rainfall, and soil type.
- Our methodology concluded that the FLTOPSIS method (AUC = 0.983) outperformed the FLANP (AUC = 0.938) for predicting landslides in the study area. We conclude that FLTOPSIS is better at solving uncertainty and ambiguity in judgement operations than FLANP.
- FLTOPSIS, thus far an infrequently used method in landslide susceptibility assessment, constitutes a promising and innovative technique for creating susceptibility maps in other landslide-prone areas, although further testing is warranted.
- Local government agencies can implement the findings of this research to manage and plan land development in susceptible landslide areas strategically.
- In the future, we recommend combining fuzzy logic with other MCDM methods, such as ELECTRE, VI-KORE, and ELECTRE III, and comparing the results to determine which combination achieves the most reliable landslide susceptibility map.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Location of landslides in the study area: the Saqqez-Marivan road in the Kurdistan Province of Iran.

**Figure 2.**Different types of landslides observed along the Saqqez-Marivan road in the Kurdistan Province of Iran.

**Figure 3.**The thematic map of landslide conditioning factors: (

**a**) slope degree, (

**b**) aspect, (

**c**) elevation, (

**d**) lithology, (

**e**) land use, (

**f**) distance to fault, (

**g**) rainfall, (

**h**) curvature, (

**i**) soil, (

**j**) distance to river, (

**k**) distance to the Saqqez-Marivan road in the Kurdistan Province of Iran.

**Table 1.**Different combinations of percentage allocations of data into the of training and validation datasets for landslide susceptibility assessments.

Model | Fuzzy ANP | Fuzzy TOPSIS | ||||
---|---|---|---|---|---|---|

Criteria | The Weight Final | Rank | Distance to Positive Ideal | Distance to Negative Ideal | Relative Proximity to the Ideal Solution | Rank |

Distance to river | 0.095 | 5 | 0.232 | 0.108 | 0.317 | 8 |

Distance to road | 0.141 | 1 | 0.108 | 0.235 | 0.675 | 1 |

Land use | 0.080 | 9 | 0.343 | 0.105 | 0.295 | 10 |

Lithology | 0.028 | 11 | 0.355 | 0.111 | 0.283 | 11 |

Rainfall | 0.108 | 3 | 0.117 | 0.185 | 0.598 | 2 |

Slope | 0.089 | 7 | 0.201 | 0.108 | 0.343 | 6 |

Soil | 0.112 | 2 | 0.151 | 0.110 | 0.423 | 4 |

curvature | 0.060 | 10 | 0.343 | 0.111 | 0.303 | 9 |

Aspect | 0.095 | 6 | 0.189 | 0.091 | 0.337 | 7 |

Distance to fault | 0.104 | 4 | 0.145 | 0.149 | 0.502 | 3 |

Elevation | 0.088 | 8 | 0.168 | 0.112 | 0.385 | 5 |

**Table 3.**Proportional distribution of landslides in five different landslide classes, based on fuzzy ANP and fuzzy TOPSIS.

Landslide Classes | Fuzzy ANP | Fuzzy TOPSIS | ||
---|---|---|---|---|

Class Area (%Pixels) | Landslide (%Pixels) | Class Area (%Pixels) | Landslide (%Pixels) | |

Very low susceptibility | 30.65 | 0.00 | 24.75 | 0.00 |

Low susceptibility | 24.70 | 3.33 | 23.98 | 0.00 |

Moderate susceptibility | 21.39 | 6.67 | 26.37 | 10.00 |

High susceptibility | 14.74 | 3.33 | 15.53 | 6.67 |

Very high susceptibility | 8.52 | 86.67 | 9.37 | 83.33 |

Row | Models | Validating Dataset |
---|---|---|

1 | Fuzzy TOPSIS | 0.983 |

2 | Fuzzy ANP | 0.938 |

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© 2023 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 (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Tavakolifar, R.; Shahabi, H.; Alizadeh, M.; Bateni, S.M.; Hashim, M.; Shirzadi, A.; Ariffin, E.H.; Wolf, I.D.; Shojae Chaeikar, S.
Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran. *Land* **2023**, *12*, 1151.
https://doi.org/10.3390/land12061151

**AMA Style**

Tavakolifar R, Shahabi H, Alizadeh M, Bateni SM, Hashim M, Shirzadi A, Ariffin EH, Wolf ID, Shojae Chaeikar S.
Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran. *Land*. 2023; 12(6):1151.
https://doi.org/10.3390/land12061151

**Chicago/Turabian Style**

Tavakolifar, Rahim, Himan Shahabi, Mohsen Alizadeh, Sayed M. Bateni, Mazlan Hashim, Ataollah Shirzadi, Effi Helmy Ariffin, Isabelle D. Wolf, and Saman Shojae Chaeikar.
2023. "Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran" *Land* 12, no. 6: 1151.
https://doi.org/10.3390/land12061151