Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

Zhou, Xinzhi; Wen, Haoran; Zhang, Yalan; Xu, Jiahui; Zhang, Wengang

doi:10.1016/j.gsf.2021.101211

Cited by 188 publications

(88 citation statements)

References 78 publications

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“…Through feature selection, the results of model effect optimization can be achieved. The main idea of feature recursive elimination method is to eliminate the factor with the smallest ranking criterion score at each time on the basis of all the initial influencing factors and to construct the model repeatedly until the final feature set is obtained [ 43 ]; the ranking of features is obtained at the same time.…”

Section: Methodsmentioning

confidence: 99%

Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China

Zhang

Wen

Liao

et al. 2022

Sensors

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‘Resilience’ is a new concept in the research and application of urban construction. From the perspective of building adaptability in a mountainous environment and maintaining safety performance over time, this paper innovatively proposes machine learning methods for evaluating the resilience of buildings in a mountainous area. Firstly, after considering the comprehensive effects of geographical and geological conditions, meteorological and hydrological factors, environmental factors and building factors, the database of building resilience evaluation models in a mountainous area is constructed. Then, machine learning methods such as random forest and support vector machine are used to complete model training and optimization. Finally, the test data are substituted into models, and the models’ effects are verified by the confusion matrix. The results show the following: (1) Twelve dominant impact factors are screened. (2) Through the screening of dominant factors, the models are comprehensively optimized. (3) The accuracy of the optimization models based on random forest and support vector machine are both 97.4%, and the F1 scores are greater than 94.4%. Resilience has important implications for risk prevention and the control of buildings in a mountainous environment.

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Section: Methodsmentioning

confidence: 99%

Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China

Zhang

Wen

Liao

et al. 2022

Sensors

Self Cite

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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%

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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“…As a result of all advantages and conveniences attributed to RFE, it is now widely adopted in land use classification, biological information identification, landslide sensitivity assessment, etc. [25][26][27]. To explain, RFE can intentionally omit particular features through recursion, build model on rest data, and filter the optimum combination based on modeling results.…”

Section: Introductionmentioning

confidence: 99%

Research on Classification of Open-Pit Mineral Exploiting Information Based on OOB RFE Feature Optimization

Zhou

Tian

Chen

et al. 2022

Sensors

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Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions.

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Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

Cited by 188 publications

References 78 publications

Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China

Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Research on Classification of Open-Pit Mineral Exploiting Information Based on OOB RFE Feature Optimization

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