**Table 3.** *Spatial relationship between landslide-conditioning factors and landslides.*

#### *Landslides*

*Evaluation of Landslide Susceptibility of Şavşat District of Artvin Province (Turkey) Using… DOI: http://dx.doi.org/10.5772/intechopen.99864*

Non-irrigated arable lands (CORINE land cover code 211), agricultural areas within natural vegetation (243), mixed agricultural areas (242), discontinuous urban structure (112), and bare rocks (332) were determined as landslide sensitive areas. The scattered settlements in the villages cause uncontrolled excavations, which in turn triggers landslides. In the landslide susceptibility study conducted by Erener et al. [34] in Şavşat district, it was reported that landslide activity increased in areas where the original vegetation was removed or changed. In the same study, it was determined that farming areas, irrigated or dry, were more susceptible to landslides. Researchers attributed this to the deforestation in agricultural areas.

#### **4.2 Validation and comparison of landslide susceptibility models**

Thi Ngo et al. [7] stated that it is important to identify landslide-prone areas with high accuracy and to use an appropriate metric for the performance evaluation to produce a reliable landslide susceptibility map. The performances of the models used in the production of landslide susceptibility maps are mostly evaluated using the receiver-operating characteristics (ROC) curve [28, 38, 45, 60, 71–73]. Therefore, in this study, the receiver-operating characteristic-area under the curve (ROC-AUC) approach was applied to evaluate and measure the performances of ML models. The ROC curve is a graph showing the true positive rate (TPR or sensitivity) on the vertical axis and the false positive rate (FPR or 1-specificity) on the horizontal axis. In the ROC curve, the most important indicator used to evaluate the accuracy or performance of the susceptibility model is the AUC. AUC takes values between 0.5 and 1 [71]. An AUC value close to 1.0 indicates high performance of the model and close to 0.5 indicates low performance of the model. On the contrary, Chen et al. [74] and Wang et al. [17] stated that the AUC value can be classified in five classes: poor (0.5–0.6), moderate (0.6–0.7), good (0.7–0.8), very good (0.8–0.9), and excellent (0.9–1.0).

In the study, success rate and prediction rate curves were created using training and validation data sets, respectively. The success rate curve is used to understand how well the models used to produce landslide susceptibility maps to classify existing landslide areas [74]. In this study, the AUC values of the success rate curves for the GBM, RF, and XGBoost models were calculated as 91.6%, 98.4%, and 98.6%, respectively (**Figure 9a**). Since the success rate curve is produced using the training

**Figure 9.** *a) Success rate b) prediction rate curves for ML models.*

data set, it is not an appropriate indicator to evaluate the predictive capabilities of the models [21, 42]. The prediction rate curve should be used to evaluate the prediction capabilities of the models [75]. The prediction rate curve shows how well the models predict unknown or probable future landslides [5]. The AUC values of the prediction rate curves produced for the GBM, RF, and XGBoost models were calculated as 91.4%, 97.9% and 98.1%, respectively (**Figure 9b**). AUC value being close to 1.0 in three models show, according to the classification made by Chen et al. [74] and Wang et al. [17], that their performances, i.e., their prediction capacities, are excellent.

#### **5. Conclusions**

In this study, RF, GBM, and XGBoost algorithms were used for landslide susceptibility mapping of Şavşat district of Artvin Province. The performances of these models were evaluated using success rate and prediction rate curves. According to the AUC values, the models used in the study showed excellent performance. However, the XGBoost model outperformed the other two models in landslide susceptibility mapping of the study area. Therefore, it was concluded that the susceptibility map produced by the XGBoost model can help decision makers and planners in reducing the risks caused by landslides in the region and in land use planning. In this study, 11 factors—altitude, aspect, curvature, distance to drainage network, distance to faults, distance to roads, land cover, lithology, slope, slope length, and TWI—were used based on the availability of the data, geo-environmental conditions of the study area, and literature survey. As a result of the study, it was concluded that the main factor governing the landslides in the study area in all three models is lithology. The artificial factors that trigger landslides across the province of Artvin, as in Şavşat district, are uncontrolled excavation works (usually road widening), uncontrolled explosive excavations, and uncontrolled agricultural land irrigation. In this respect, providing basic disaster awareness trainings to citizens residing in areas susceptible to landslides in the study area and trainings on the causes, effects, and consequences of landslides will be beneficial in terms of risk reduction. Similarly, taking into account landslide susceptibility maps in selecting dwelling zones in rural areas and in determining the routes through which infrastructure facilities such as drinking water, natural gas, electricity, and sewerage will pass, will be effective in reducing the risks associated with landslides in the study area.

#### **Conflict of interest**

The authors declare no conflict of interest.

*Evaluation of Landslide Susceptibility of Şavşat District of Artvin Province (Turkey) Using… DOI: http://dx.doi.org/10.5772/intechopen.99864*

#### **Author details**

Halil Akinci1 \*, Mustafa Zeybek<sup>2</sup> and Sedat Dogan3

1 Department of Geomatics Engineering, Artvin Çoruh University, Artvin, Turkey

2 Güneysınır Vocational School, Selcuk University, Konya, Turkey

3 Department of Geomatics Engineering, Ondokuz Mayis University, Samsun, Turkey

\*Address all correspondence to: halil.akinci@artvin.edu.tr

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 6**
