Evaluation of Landslide Susceptibility of Şavşat District of Artvin Province (Turkey) Using Machine Learning Techniques

Akıncı, Halil; Zeybek, Mustafa; Doğan, Sedat

doi:10.5772/intechopen.99864

Cited by 8 publications

(4 citation statements)

References 67 publications

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“…These factors are related to topography (T_E, T_F_acc, T_FLAT, T_NW, T_TPI, T_S, T_TWI, T_VRM, T_W), seismicity (S_Epic), geolithology (G_TA, G_SSC, G_CM, G_C, G_DDB, G_LG, G_SC, G_SSM, G_CCL, G_ML), land cover (LU_urb, LU_grs, LU_for, LU_nat), morphology (D_riv, D_road), and climate (C_p_max). It is well known [48][49][50][51] that the slope length and steepness factor (T_Ls) is one of the main landslide predictors, being a parameter used to characterize the effects of topography and hydrology on soil loss [52,53]. In our study, the T_Ls showed the highest coefficient in predicting the totality of recorded events and one of the most predicting variables for the other landslide types, evidencing its influence in the evolution of landslide types.…”

Section: Correlation Analysissupporting

confidence: 54%

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Assessing Landslide Susceptibility by Coupling Spatial Data Analysis and Logistic Model

et al. 2022

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Landslides represent one of the most critical issues for landscape managers. They can cause injuries and loss of human life and damage properties and infrastructure. The spatial and temporal distribution of these detrimental events makes them almost unpredictable. Studies on landslide susceptibility assessment can significantly contribute to prioritizing critical risk zones. Further, landslide prevention and mitigation and the relative importance of the affecting drivers acquire even more significance in areas characterized by seismicity. This study aimed to investigate the relationship between a set of environmental variables and the occurrence of landslide events in an area of the Apulia Region (Italy). Logistic regression was applied to a landslide-prone area in the Apulia Region (Italy) to identify the main causative factors using a large dataset of environmental predictors (47). The results of this case study show that the logistic regression achieved a good performance, with an AUC (Area Under Curve) >70%. Therefore, the model developed would be a useful tool to define and assess areas for landslide occurrence and contribute to implementing risk mitigation strategy and land use policy.

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Section: Correlation Analysissupporting

confidence: 54%

“…Concave shape also affects the development of rotational/translational mass movement, reflecting the importance of the hydrological regime in slope stability. In these areas, a convergence of surface and subsurface water streams saturates rapidly the soil, which becomes more prone to movements [51].…”

Section: Correlation Analysismentioning

confidence: 99%

Assessing Landslide Susceptibility by Coupling Spatial Data Analysis and Logistic Model

et al. 2022

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“…While XGBoost shares some parameters with other tree-based models, it involves additional hyperparameters to control the overfitting concern, enhance precision, and mitigate forecasting variance [82]. This study develops the landslide susceptibility model using the "XGBoost" package in R 4.0.2 software, which provides powerful capabilities for classification tasks [83]. In this research, three general parameters were chosen for us to alter in the XGBoost algorithm for LSM application: nrounds (the maximum number of boosting repetitions), subsample (the subsample ratio of the training instance), and colsample_bytree (the subsample ratio of columns while constructing each tree).…”

Section: Rfmentioning

confidence: 99%

Deep Learning and Machine Learning Models for Landslide Susceptibility Mapping with Remote Sensing Data

Hussain,

Chen,

Zheng

et al. 2023

Remote Sensing

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Karakoram Highway (KKH) is an international route connecting South Asia with Central Asia and China that holds socio-economic and strategic significance. However, KKH has extreme geological conditions that make it prone and vulnerable to natural disasters, primarily landslides, posing a threat to its routine activities. In this context, the study provides an updated inventory of landslides in the area with precisely measured slope deformation (Vslope), utilizing the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) and PS-InSAR (persistent scatterer interferometric synthetic aperture radar) technology. By processing Sentinel-1 data from June 2021 to June 2023, utilizing the InSAR technique, a total of 571 landslides were identified and classified based on government reports and field investigations. A total of 24 new prospective landslides were identified, and some existing landslides were redefined. This updated landslide inventory was then utilized to create a landslide susceptibility model, which investigated the link between landslide occurrences and the causal variables. Deep learning (DL) and machine learning (ML) models, including convolutional neural networks (CNN 2D), recurrent neural networks (RNNs), random forest (RF), and extreme gradient boosting (XGBoost), are employed. The inventory was split into 70% for training and 30% for testing the models, and fifteen landslide causative factors were used for the susceptibility mapping. To compare the accuracy of the models, the area under the curve (AUC) of the receiver operating characteristic (ROC) was used. The CNN 2D technique demonstrated superior performance in creating the landslide susceptibility map (LSM) for KKH. The enhanced LSM provides a prospective modeling approach for hazard prevention and serves as a conceptual reference for routine management of the KKH for risk assessment and mitigation.

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“…Nonlinear models better identify the nonlinear relationship between predictor variables and CW-SOC content, solve the problem of spatial autocorrelation [14,21], and have good generalization performance. This is especially the case for machine learning methods based on decision tree models, such as random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) [22]. They show good performance in predicting SOC content, especially when dealing with complex and high-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Coastal Wetland Soil Organic Carbon Content in Western Bohai Bay Using Remote Sensing, Climate, and Topographic Data

Zhang,

Kou,

Liu

et al. 2023

Remote Sensing

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Coastal wetland soil organic carbon (CW-SOC) is crucial for wetland ecosystem conservation and carbon cycling. The accurate prediction of CW-SOC content is significant for soil carbon sequestration. This study, which employed three machine learning (ML) methods, including random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost), aimed to estimate CW-SOC content using 98 soil samples, SAR images, optical images, and climate and topographic data. Three statistical metrics and leave-one-out cross-validation were used to evaluate model performance. Optimal models using different ML methods were applied to predict the spatial distribution of CW-SOC content. The results showed the following: (1) The models built using optical images had higher predictive accuracy than models built using synthetic aperture radar (SAR) images. The model that combined SAR images, optical images, and climate data demonstrated the highest prediction accuracy. Compared to the model using only optical images and SAR images, the prediction accuracy was improved by 0.063 and 0.115, respectively. (2) Regardless of the combination of predictive variables, the XGBoost method achieved higher prediction accuracy than the RF and GBM methods. (3) Optical images were the main explanatory variables for predicting CW-SOC content, explaining more than 65% of the variability. (4) The CW-SOC content predicted by the three ML methods showed similar spatial distribution characteristics. The central part of the study area had higher CW-SOC content, while the southern and northern regions had lower levels. This study accurately predicted the spatial distribution of CW-SOC content, providing data support for ecological environmental protection and carbon neutrality of coastal wetlands.

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Evaluation of Landslide Susceptibility of Şavşat District of Artvin Province (Turkey) Using Machine Learning Techniques

Cited by 8 publications

References 67 publications

Assessing Landslide Susceptibility by Coupling Spatial Data Analysis and Logistic Model

Assessing Landslide Susceptibility by Coupling Spatial Data Analysis and Logistic Model

Deep Learning and Machine Learning Models for Landslide Susceptibility Mapping with Remote Sensing Data

Estimation of Coastal Wetland Soil Organic Carbon Content in Western Bohai Bay Using Remote Sensing, Climate, and Topographic Data

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