An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost

Zhou, Xinzhi; Wen, Haoran; Zhang, Hui; Zhang, Wengang

doi:10.1080/10106049.2022.2076928

Cited by 81 publications

(32 citation statements)

References 78 publications

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“…Based on the training and validation datasets, the model successfully distinguishes the landslide-prone areas in the study area. Our results support previous studies showing that coupled models can significantly reduce overfitting and noise problems in the modeling process [ 13 , 23 , 54 , 57 , 58 , 59 , 60 , 61 , 62 ]. The novelty of our method is that we consider the combination of statistical models and machine learning models, which can perform well in solving the problem of poor performance of a single model.…”

Section: Discussionsupporting

confidence: 91%

Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture

Deng

et al. 2022

IJERPH

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At present, landslide susceptibility assessment (LSA) based on landslide characteristics in different areas is an effective measure for landslide management. Nujiang Prefecture in China has steep mountain slopes, a large amount of water and loose soil, and frequent landslide disasters, which have caused a large number of casualties and economic losses. This paper aims to understand the characteristics and formation mechanism of regional landslides through the evaluation of landslide susceptibility so as to provide relevant references and suggestions for spatial planning and disaster prevention and mitigation in Nujiang Prefecture. Based on the grid cell, this study selected 10 parameters, namely elevation, slope, aspect, lithology, proximity to faults, proximity to road, proximity to rivers, normalized difference vegetation index (NDVI), land-use type, and precipitation. Support vector machine (SVM), certainty factor method (CF), and deterministic coefficient method–support vector machine (CF-SVM) were used to evaluate the landslide susceptibility in Nujiang Prefecture. According to these three models, the study area was divided into five landslide susceptibility grades, including extremely high susceptibility, high susceptibility, moderate susceptibility, low susceptibility, and very low susceptibility. Receiver operating characteristic curve (ROC) was applied to verify the accuracy of the model. The results showed that CF model (ROC = 0.865), SVM model (ROC = 0.892), CF-SVM model (ROC = 0.925), and CF-SVM model showed better performance. Therefore, CF-SVM model results were selected for analysis. The study found that the characteristics of high and extremely high landslide-prone areas in Nujiang Prefecture have the following characteristics: intense human activities, large density of buildings and arable land, rich water resources, good economic development, perfect transportation facilities, and complex topography and landform. In addition, there is a finding inconsistent with our common sense that the distribution of landslide disasters in the study area does not decrease with the increase of NDVI value. This is because the Nujiang River basin is a high mountain canyon area with low rock strength, barren soil, and underdeveloped vegetation and root system. In an area with large slope, the probability of landslide disaster will increase with the increase of NDVI. The CF-SVM coupling model adopted in this study is a good first attempt in the study of landslide hazard susceptibility in Nujiang Prefecture.

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Section: Discussionsupporting

confidence: 91%

Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture

Deng

et al. 2022

IJERPH

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“…The core idea of SHAP is to calculate the marginal contribution of features to the model output, and then interpret the “black box model” at both global and local levels. During model training or testing, a corresponding prediction value is generated for each sample, and the SHAP value is the corresponding value attributed to each feature in the sample ( 41 ). In the figure, the horizontal axis represent the SHAP values (the distribution of feature effects on the model output) and the vertical axis represent the feature ranking corresponding to the sum of the 400 sample SHAP values.…”

Section: Discussionmentioning

confidence: 99%

Self-attention random forest for breast cancer image classification

Shi

Chen

et al. 2023

Front. Oncol.

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IntroductionEarly screening and diagnosis of breast cancer can not only detect hidden diseases in time, but also effectively improve the survival rate of patients. Therefore, the accurate classification of breast cancer images becomes the key to auxiliary diagnosis.MethodsIn this paper, on the basis of extracting multi-scale fusion features of breast cancer images using pyramid gray level co-occurrence matrix, we present a Self-Attention Random Forest (SARF) model as a classifier to explain the importance of fusion features, and can perform adaptive refinement processing on features, thus, the classification accuracy can be improved. In addition, we use GridSearchCV technique to optimize the hyperparameters of the model, which greatly avoids the limitation of artificially selected parameters.ResultsTo demonstrate the effectiveness of our method, we perform validation on the breast cancer histopathological image-BreaKHis. The proposed method achieves an average accuracy of 92.96% and a micro average AUC value of 0.9588 for eight-class classification, and an average accuracy of 97.16% and an AUC value of 0.9713 for binary classification on BreaKHis dataset.DiscussionFor the sake of verify the universality of the proposed model, we also conduct experiments on MIAS dataset. An excellent average classification accuracy is 98.79% on MIAS dataset. Compared to other state-of-the-art methods, the experimental results demonstrate that the performance of the proposed method is superior to that of others. Furthermore, we can analyze the influence of different types of features on the proposed model, and provide theoretical basis for further optimization of the model in the future.

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“…Zhou et al [140] proposes a novel interpretable model based on the Shapley additive explanation and XGBoost to interpret landslides susceptibility evaluation at global and local levels. The proposed model delivered 0.75 in accuracy and an AUC value of 0.83 for the test sets.…”

Section: Author Year Ensemble Methodsmentioning

confidence: 99%

“…It was developed to provide a more general approach to the probabilistic prediction method. XGBoost [129,140,141] belongs to the gradient boosting family and is an effective supervised classification model. It is a preferred ensemble technique mainly because of its ability to prevent over-fitting issues by using bagging-bootstrap aggregation.…”

Section: Ensemble Techniquesmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

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Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

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An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost

Cited by 81 publications

References 78 publications

Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture

Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture

Self-attention random forest for breast cancer image classification

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

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