Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach

Iban, Muzaffer Can; Bilgilioğlu, Süleyman Sefa

doi:10.1007/s00477-023-02392-6

Cited by 23 publications

(13 citation statements)

References 100 publications

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“…Leaf-aware trees can sometimes cause overfitting, especially with smaller datasets. Limiting shaft depth can help prevent overfitting (Iban & Bilgilioglu, 2023;Zeng et al, 2024).…”

Section: Lighgbmmentioning

confidence: 99%

Assessing the relationship between landslide susceptibility and land cover change using machine learning

Nguyen Huu,

Vu Cong,

Bretcan

et al. 2024

Vietnam J. Earth Sci.

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Landslides are natural disasters most frequent in the mountain region of Vietnam, producing critical damage to human lives and assets. Therefore, precisely identifying the landslide occurrence probability within the region is essential in supporting decision-makers or developers in establishing effective strategies for reducing the damage. This study is aimed at developing a methodology based on machine learning, namely Xgboost (XGB), lightGBM, K-Nearest Neighbors (KNN), and Bagging (BA) for assessing the connection of land cover change to landslide susceptibility in Da Lat City, Vietnam. 202 landslide locations and 13 potential drivers became input data for the model. Various statistical indices, namely the root mean square error (RMSE), the area under the curve (AUC), and mean absolute error (MAE), were used to evaluate the proposed models. Our findings indicate that the Xgboost model was better than other models, as shown by the AUC value of 0.94, followed by LightGBM (AUC=0.91), KNN (AUC=0.87), and Bagging (AUC=0.81). In addition, urban areas increased during 2017-2023 from 25 km² to 30 km² in very high landslide susceptibility areas. Our approach can be applied to test the other regions in Vietnam. Our findings might represent a necessary tool for land use planning strategies to reduce damage from natural disasters and landslides.

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“…Leaf-aware trees can sometimes cause overfitting, especially with smaller datasets. Limiting shaft depth can help prevent overfitting (Iban & Bilgilioglu, 2023;Zeng et al, 2024).…”

Section: Lighgbmmentioning

confidence: 99%

Assessing the relationship between landslide susceptibility and land cover change using machine learning

Nguyen Huu,

Vu Cong,

Bretcan

et al. 2024

Vietnam J. Earth Sci.

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“…Another approach used for the evaluation of the importance of variables (i.e., conditioning factors) is based on eXplainable Artificial Intelligence (XAI), which allows analyzing the importance of variables with a broader approach [33]. Among the XAI methods are SHAP (SHapley Additive eXplanation) which has been applied in several studies for landslide susceptibility analysis [33][34][35][36] which is based on cooperative game theory (CGP) to explain the prediction results and thus improving the explainability of ML models [35]. SHAP is easy to operate [36] and can be presented graphically [34].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods

Bravo-López

Sellers

Fernández

2023

Land

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Landslides are events that cause great impact in different parts of the world. Their destructive capacity generates loss of life and considerable economic damage. In this research, several Machine Learning (ML) methods were explored to select the most important conditioning factors, in order to evaluate the susceptibility to rotational landslides in a sector surrounding the city of Cuenca (Ecuador) and with them to elaborate landslide susceptibility maps (LSM) by means of ML. The methods implemented to analyze the importance of the conditioning factors checked for multicollinearity (correlation analysis and VIF), and, with an ML-based approach called feature selection, the most important factors were determined based on Classification and Regression Trees (CART), Feature Selection with Random Forests (FS RF), and Boruta and Recursive Feature Elimination (RFE) algorithms. LSMs were implemented with Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) methods considering a landslide inventory updated to 2019 and 15 available conditioning factors (topographic (10), land cover (3), hydrological (1), and geological (1)), from which, based on the results of the aforementioned analyses, the six most important were chosen. The LSM were elaborated considering all available factors and the six most important ones, with the previously mentioned ML methods, and were compared with the result generated by an Artificial Neural Network with resilient backpropagation (ANN rprop-) with six conditioning factors. The results obtained were validated by means of AUC-ROC value and showed a good predictive capacity for all cases, highlighting those obtained with XGBoost, which, in addition to a high AUC value (>0.84), obtained a good degree of coincidence of landslides at high and very high susceptibility levels (>72%). Despite the findings of this research, it is necessary to study in depth the methods applied for the development of future research that will contribute to developing a preventive approach in the study area.

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“…Pradhan et al [27] successfully elucidated the weightings of the internal features and prediction outcomes of a CNN model using SHAP. Iban et al [28] utilized SHAP to elucidate three ensemble learning models, namely XGBoost, NGBoost, and LightGBM. The work of numerous researchers has confirmed that SHAP offers superior interpretability and visual interpretability.…”

Section: Introductionmentioning

confidence: 99%

Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China

Yao,

Chen,

Zhan

et al. 2023

Applied Sciences

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Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) results, achieving more precise and detailed susceptibility mapping. We utilized optical remote sensing images, the information value (IV) model, and fourteen influencing factors (elevation, slope, aspect, roughness, profile curvature, plane curvature, lithology, distance to faults, land use type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), distance to rivers, distance to roads, and annual precipitation) to establish the IV-CatBoost landslide susceptibility mapping method. Subsequently, the Sentinel-1A ascending data from January 2021 to March 2023 were utilized to derive the deformation rates within the city of Lishui in the southern region of China. Based on the outcomes derived from IV-CatBoost and SBAS-InSAR, a discernment matrix was formulated to rectify inaccuracies in the partitioned regions, leading to the creation of a refined information value CatBoost integration (IVCI) landslide susceptibility mapping model. In the end, we utilized optical remote sensing interpretations alongside surface deformations obtained from SBAS-InSAR to cross-verify the excellence and accuracy of IVCI. Research findings indicate a distinct enhancement in susceptibility levels across 165,784 grids (149.20 km2) following the integration of SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images closely correspond to the trends of SBAS-InSAR cumulative deformation, reflecting a high level of consistency with field-based conditions. These improved classifications effectively enhance the refinement of landslide susceptibility mapping. The refined susceptibility mapping approach proposed in this paper effectively enhances landslide prediction accuracy, providing valuable technical reference for landslide hazard prevention and control in the Lishui region.

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Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach

Cited by 23 publications

References 100 publications

Assessing the relationship between landslide susceptibility and land cover change using machine learning

Assessing the relationship between landslide susceptibility and land cover change using machine learning

Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods

Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China

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