Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping

Song, Jiahui; Wang, Yi; Fang, Zhice; Peng, Ling; Hong, Haoyuan

doi:10.1109/jstars.2020.3014143

Cited by 37 publications

(16 citation statements)

References 68 publications

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“…Researchers also have used the Ridge regression method for LCFs importance analysis [45]. Other popular methods for feature selection and analysis includes relative risk regression analyse [45], fractal analysis [48], resampling scheme analysis and Pearson's correlation analysis [49], correlation-based features selections (CFS) [50], frequency ratio (FR) [51], fuzzy and weights of LCFs using SVM [52], principal component analysis (PCA) to select independent and significant LCFs [53], information gain method [54], GeoDetector and recursive feature elimination (RFE) method for LCFs optimization to reduce redundancy [51], interactive detector [51], one rule (one-R) [42], correlation attributes evaluation (CAE) where greater calculated average merit (AM) indicates more influence of the LCF [55], sensitivity analysis [56], Spearman's rank correlation coefficient [57], relief-F method [58], Fischer score analysis [47], and gain ratio method [59].…”

Section: Legendmentioning

confidence: 99%

“…Combining a feature selection and optimization technique with ML models or a combination of multiple ML models is called a hybrid method. The conventional techniques for selecting causative factors includes multi-collinearity analysis by calculating VIF and tolerance (TOL) [46,47,116], and co-relation methods such as Pearson's correlation analysis [49], CFS [50], CAE [55], and Spearman's rank correlation coefficient [57]. ML models were also used for feature selection, some of the models includes SVM [52] and RF [44].…”

Section: Hybrid Techniquesmentioning

confidence: 99%

“…Microsoft introduced it as a new gradient boosting framework to overcome the limitations of GBDT in massive data. RF [57,72,73,76,123,130,131] is a basic ensemble ML model used in the generation of LSM and is considered a conventional technique because of its popularity in landslide susceptibility mapping. It belongs to the Boostrap aggregation family.…”

Section: Ensemble Techniquesmentioning

confidence: 99%

“…The final prediction is the weighted sum of all the prediction nodes. Extremely randomized tree [57] is a tree-based ensemble method that is very similar to RF, which is a combination of CART and bagging. The algorithm randomly selects the splitting point in a node.…”

Section: Ensemble Techniquesmentioning

confidence: 99%

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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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Section: Legendmentioning

confidence: 99%

Section: Hybrid Techniquesmentioning

confidence: 99%

Section: Ensemble Techniquesmentioning

confidence: 99%

Section: Ensemble Techniquesmentioning

confidence: 99%

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Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

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“…The degree of rainfall infiltration into the slope body varies with the slope type, which can be characterized by plan curvature and profile curvature. Land cover affects soil erosion, especially the degree of vegetation cover, which can be expressed by the normalized difference vegetation index (NDVI) [51,52]. The land cover was downloaded at Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) and the NDVI was calculated from the near infrared and red band values in Landsat8 OLI satellite remote sensing digital images shot in April 2018:…”

Section: Landslides Susceptibility Influencing Factorsmentioning

confidence: 99%

Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

Rong

et al. 2021

Remote Sensing

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Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions.

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Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models

Chen,

Fan

2023

Stoch Environ Res Risk Assess

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Landslides are a common natural disaster that can cause casualties, property safety threats and economic losses. Therefore, it is important to understand or predict the probability of landslide occurrence at potentially risky sites. A commonly used means is to carry out a landslide susceptibility assessment based on a landslide inventory and a set of landslide contributing factors. This can be readily achieved using machine learning (ML) models such as logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (Xgboost), or deep learning (DL) models such as convolutional neural network (CNN) and long short time memory (LSTM). As the input data for these models, landslide contributing factors have varying influences on landslide occurrence. Therefore, it is logically feasible to select more important contributing factors and eliminate less relevant ones, with the aim of increasing the prediction accuracy of these models. However, selecting more important factors is still a challenging task and there is no generally accepted method. Furthermore, the effects of factor selection using various methods on the prediction accuracy of ML and DL models are unclear. In this study, the impact of the selection of contributing factors on the accuracy of landslide susceptibility predictions using ML and DL models was investigated. Four methods for selecting contributing factors were considered for all the aforementioned ML and DL models, which included Information Gain Ratio (IGR), Recursive Feature Elimination (RFE), Particle Swarm Optimization (PSO), Least Absolute Shrinkage and Selection Operators (LASSO) and Harris Hawk Optimization (HHO). In addition, autoencoder-based factor selection methods for DL models were also investigated. To assess their performances, an exhaustive approach was adopted, testing all possible selection cases of contributing factors, the results of which served as the benchmark. The results confirmed that using more important contributing factors improved the prediction accuracy of the ML models considered. However, it was interesting to find that the selection of contributing factors using IGR and RFE reduced the predictive power of the DL models. For the DL models, using an autoencoder architecture improved their prediction performance. The study also found that the choice of factor selection methods was far more effective than the selection of contributing factors in improving the prediction accuracy of landslide susceptibility.

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Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping

Cited by 37 publications

References 68 publications

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models

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