# A Novel Rule-Based Approach in Mapping Landslide Susceptibility

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## Abstract

**:**

## 1. Introduction

## 2. Description of the Study Region

## 3. Materials and Methods

#### 3.1. Landslide-conditioning Factors

#### 3.2. Description of DoTRules with Modifications for LSM

_{1}, i

_{2}, …, i

_{m}}, where each pixel i in the training set I has a value x

_{ij}for each of the independent landslide-conditioning criteria J = {j

_{1}, j

_{2}, …, j

_{n}}. In this study, 11 conditioning criteria (Table 1) exist and all of them are discretized variables. These discretized landslide-conditioning variables can be either derived from native categorical data (e.g., land-use class, geology etc.) or re-classified continuous data (e.g., distance from roads, distance to coastal lines etc.). Therefore, each x

_{ij}can adopt one of a fixed set of possible classes H that is specific to that criterion (Table 1). Although each criterion j has a different set of classes H, for sake of simplicity, here we do not index H by j. Each pixel i has also a corresponding landslide label l

_{i}from L{1: Landslide, 0: Non-landslide}.

_{hj}) is calculated by the proportion of cells in each class:

_{j}. Here, the lower entropy stands for the higher priority of landslide-conditioning factor being assessed, which is represented by the ordered set of criteria priority J’.

_{ij}as per criteria priority J’ in order to form a rule-set D. The concatenation of two or more characters is the string formed by them in a series (i.e., the concatenation of 31, A7, and 5# is 31A75#). Equation 4 illustrates the pixel values for criteria ranked in order of priority (i.e., the lowest entropy) concatenated for each pixel (row) i, thereby creating a unique rule for each pixel in the training dataset.

#### 3.3. Methodology Implementation

## 4. Results

#### 4.1. Validation of the Susceptibility Map Using AUC Estimate

#### 4.2. Validation of the LSM by Overlaying Technique

## 5. Discussion

#### 5.1. Model Transparency and Spatial Information Extraction

#### 5.2. Reducing Subjectivity of Final LSM

#### 5.3. Decision Aiding and Planning

#### 5.4. Limitation of the Proposed Methodology in LSM

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**A location map of the study area. Dots represent the location of occurred various landslide events.

**Figure 2.**Eleven applied landslide-conditioning factors involving: (

**a**) Slope; (

**b**) aspect; (

**c**) distance to main streams; (

**d**) distance to coastal lines; (

**e**) NDVI; (

**f**) mean annual rainfall; (

**g**) distance to roads; (

**h**) geology; (

**i**) distance to faults; (

**j**) elevation; (

**k**) land-use, and (

**l**) landslide/non-landslide inventory database used for training the model.

**Figure 3.**The proposed If-Then rules based on a 2D scatter plot to classify the outcomes of landslide and non-landslide occurrence probability (resistance). Here, VH, H, M, L, and VL stand for very high, high, moderated, low, and very low susceptibilities. Grey points represent sample pixels of LSM for demonstration.

**Figure 5.**Achieved results of methodology implementation including (

**a**) non-landslide occurrence probability (resistance), (

**b**) landslide probability, (

**c**) entropy (i.e., uncertainty) of susceptibility mapping, and (

**d**) LSM.

**Table 1.**Selected landslide-conditioning factors based on literature review, relevant data source, description and number of discrete classes (H).

Criteria | Data Source | Description | H |
---|---|---|---|

1. Slope | Mineral Resources Tasmania (MRT) | This is the slope angle derived from a digital elevation model (DEM) of the 10 metre Lidar DEM. | 9 |

2. Aspect | Mineral Resources Tasmania (MRT) | The compass direction that a slope faces derived from the same source as slope. | 9 |

3. Mainstreams | Land Information System Tasmania (LIST) | The relative Euclidian distance of each desired pixel from the closet mainstream. | 9 |

4. Coastal lines | Land Information System Tasmania (LIST) | The relative Euclidian distance of each desired pixel from coastal lines. | 10 |

5. NDVI | Australian Bureau of Meteorology | The normalized difference vegetation index (NDVI) representing vegetation density and condition from Jun 2017 to Jun 2018. | 9 |

6. Rainfall | Australian Bureau of Meteorology | A monthly average of a 30 years rainfall (base climatological datasets) from 1961–1990. | 8 |

7. Road | Land Information System Tasmania (LIST) | The relative Euclidian distance of each desired pixel from the closet road. | 9 |

8. Geology | Mineral Resources Tasmania (MRT) | This Tasmania Geology map is derived from the 1:250,000 scale digital geology of Tasmania. | 10 |

9. Faults | Mineral Resources Tasmania (MRT) | The relative Euclidian distance of each desired pixel from the closet geological fault. | 9 |

10. Elevation | Mineral Resources Tasmania (MRT) | The representation of the land surface elevation from 10 metre Lidar source. | 9 |

11. Land use | the Australian Land Use and Management (ALUM) | The Tasmanian land use map containing 116 land-use sub-classes for the current study area. | 10 |

12. Landslides | Mineral Resources Tasmania (MRT) | A number of 641 records containing both active and inactive landslides. | - |

Rank | Variable Name | Entropy Score |
---|---|---|

1 | Coastal lines | 0.272 |

2 | Elevation | 0.378 |

3 | Rainfall | 0.437 |

4 | Land use | 0.488 |

5 | Geology | 0.540 |

6 | NDVI | 0.557 |

7 | Road | 0.579 |

8 | Slope | 0.659 |

9 | Faults | 0.659 |

10 | Aspect | 0.664 |

11 | Mainstreams | 0.671 |

Rule-set ID | Rule (Composed of Discrete H Values) | Frequency | Matching Landslides | Entropy |
---|---|---|---|---|

1 | 1_1 | 130 | 125 | 0.163 |

2 | 1_1_3 | 79 | 75 | 0.200 |

3 | 1_1_3_9 | 35 | 34 | 0.129 |

4 | 1_1_3_9_10 | 25 | 25 | 0 |

5 | 1_1_3_9_10_7 | 10 | 10 | 0 |

6 | 1_1_3_9_10_7_1 | 7 | 7 | 0 |

7 | 1_1_3_9_10_7_1_2 | 1 | 1 | 0 |

8 | 1_1_3_9_10_7_1_2_9 | 1 | 1 | 0 |

9 | 1_1_3_9_10_7_1_2_9_5 | 1 | 1 | 0 |

10 | 1_1_3_9_10_7_1_2_9_5_2 | 1 | 1 | 0 |

Summary Statistics | Achieved Values |
---|---|

Number of Cases | 782 |

Number Correct | 677 (86.5% of total) |

AUC | 0.934 |

Std. Dev. (Area) | 0.012 |

Accuracy | 86.6% |

Sensitivity | 92.6% |

Specificity | 80.6% |

Pos Cases Missed | 29 |

Neg Cases Missed | 76 |

© 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**

Roodposhti, M.S.; Aryal, J.; Pradhan, B.
A Novel Rule-Based Approach in Mapping Landslide Susceptibility. *Sensors* **2019**, *19*, 2274.
https://doi.org/10.3390/s19102274

**AMA Style**

Roodposhti MS, Aryal J, Pradhan B.
A Novel Rule-Based Approach in Mapping Landslide Susceptibility. *Sensors*. 2019; 19(10):2274.
https://doi.org/10.3390/s19102274

**Chicago/Turabian Style**

Roodposhti, Majid Shadman, Jagannath Aryal, and Biswajeet Pradhan.
2019. "A Novel Rule-Based Approach in Mapping Landslide Susceptibility" *Sensors* 19, no. 10: 2274.
https://doi.org/10.3390/s19102274