Search Results - "Haijia Wen"

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  1. 1

    A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm by Sun, Deliang, Wen, Haijia, Wang, Danzhou, Xu, Jiahui

    Published in Geomorphology (Amsterdam, Netherlands) (01-08-2020)
    “…The choice of model parameters in landslide susceptibility mapping makes a major determinant of model accuracy. The purpose of this study is to optimize the…”
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    Journal Article
  2. 2

    Identifying the essential conditioning factors of landslide susceptibility models under different grid resolutions using hybrid machine learning: A case of Wushan and Wuxi counties, China by Liao, Mingyong, Wen, Haijia, Yang, Ling

    Published in Catena (Giessen) (01-10-2022)
    “…[Display omitted] •The effect of grid resolution on factors was investigated.•Three models for finding the essential factor are proposed.•The essential factors…”
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    Journal Article
  3. 3

    A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping by Sun, Deliang, Shi, Shuxian, Wen, Haijia, Xu, Jiahui, Zhou, Xinzhi, Wu, Jianping

    Published in Geomorphology (Amsterdam, Netherlands) (15-04-2021)
    “…The aim of this study was to develop a hybrid model (Geo-RFE-RF) for Landslide Susceptibility Mapping (LSM) predicated on GeoDetector and Random Forest (RF)…”
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    Journal Article
  4. 4

    An optimal sample selection-based logistic regression model of slope physical resistance against rainfall-induced landslide by Sun, Deliang, Wen, Haijia, Zhang, Yalan, Xue, Mengmeng

    Published in Natural hazards (Dordrecht) (2021)
    “…Due to the insufficient ability of the slope to resist deformation and frequent rainfall, geological disasters often occur on the mountain slopes, especially…”
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  5. 5

    Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector by Wang, Yue, Wen, Haijia, Sun, Deliang, Li, Yuechen

    Published in Remote sensing (Basel, Switzerland) (01-07-2021)
    “…This study aims to evaluate risk and discover the distribution law for landslides, so as to enrich landslide prevention theory and method. It first selected…”
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  6. 6

    An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping: Application in Two Areas of Three Gorges Reservoir, China by Sun, Deliang, Xu, Jiahui, Wen, Haijia, Wang, Yue

    Published in Journal of earth science (Wuhan, China) (01-12-2020)
    “…Numerous researches have been published on the application of landslide susceptibility assessment models; however, they were only applied in the same areas as…”
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  7. 7

    A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples by Sun, Deliang, Wu, Xiaoqing, Wen, Haijia, Gu, Qingyu

    Published in Geomatics, natural hazards and risk (31-12-2023)
    “…The quality of samples is crucial in constructing a data-driven landslide susceptibility model. This article aims to construct a data-driven landslide…”
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  8. 8

    A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping by Sun, Deliang, Ding, Yuekai, Wen, Haijia, Zhang, Fengtai

    Published in Earth surface processes and landforms (01-01-2024)
    “…Landslide susceptibility mapping (LSM) enables the prediction of landslide occurrences, thereby offering a scientific foundation for disaster prevention and…”
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  9. 9

    Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest by Sun, Deliang, Xu, Jiahui, Wen, Haijia, Wang, Danzhou

    Published in Engineering geology (01-02-2021)
    “…This study aims to develop two optimized models of landslide susceptibility mapping (LSM), i.e., logical regression (LR) and random forest (RF) models,…”
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    Journal Article
  10. 10

    Theoretical analysis on stress distribution characteristics around a shallow buried spherical Karst cave containing fill materials in limestone strata by Xie, Peng, Ma, Shaokun, Wen, Haijia, Li, Liangyong

    Published in Environmental earth sciences (01-02-2022)
    “…The chief objective of the article is to learn the spatial characteristics of stress distribution around a shallow buried spherical Karst cave containing fill…”
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  11. 11

    Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region by Wen, Haijia, Zhou, Xinzhi, Zhang, Chi, Liao, Mingyong, Xiao, Jiafeng

    Published in Remote sensing (Basel, Switzerland) (22-04-2023)
    “…This study aims to develop different-classification-scheme-based building-seismic-resilience (BSR)-mapping models using random forest (RF) and a support vector…”
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  12. 12

    Insights into landslide susceptibility in different karst erosion landforms based on interpretable machine learning by Sun, Deliang, Wang, Jing, Wen, Haijia, Ding, Yuekai, Gu, Qingyu, Zhang, Jialan, Zhang, Fengtai

    Published in Earth surface processes and landforms (15-03-2024)
    “…The aim of the present study was to assess differences in the conditioning factors and the performance of landslide susceptibility mapping (LSM), employing the…”
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  13. 13

    Analytical model for the load transmission law of rock bolt subjected to open and sliding joint displacements by Liu, Junfeng, Yang, Haiqing, Wen, Haijia, Zhou, Xiaoping

    “…The present study focuses on the load transmission law along the rock bolt and the efficacy for different anchored conditions. Shear displacement along the…”
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  14. 14

    Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China by Zhang, Chi, Wen, Haijia, Liao, Mingyong, Lin, Yu, Wu, Yang, Zhang, Hui

    Published in Sensors (Basel, Switzerland) (03-02-2022)
    “…'Resilience' is a new concept in the research and application of urban construction. From the perspective of building adaptability in a mountainous environment…”
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  15. 15

    A Hybrid Landslide Warning Model Coupling Susceptibility Zoning and Precipitation by Deliang Sun, Qingyu Gu, Haijia Wen, Shuxian Shi, Changlin Mi, Fengtai Zhang

    Published in Forests (01-06-2022)
    “…Landslides are one of the most severe and common geological hazards in the world. The purpose of this research is to establish a coupled landslide warning…”
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  16. 16

    A susceptibility mapping model of earthquake-triggered slope geohazards based on geo-spatial data in mountainous regions by Wen, Haijia

    Published in Georisk (02-01-2015)
    “…The main purpose of this paper is to develop a susceptibility mapping model of earthquake-trigged slope geohazards based on geo-spatial data in mountainous…”
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  17. 17

    Landslides in Forests around the World: Causes and Mitigation by Haijia Wen, Weile Li, Chong Xu, Hiromu Daimaru

    Published in Forests (01-03-2023)
    “…Landslides are a common natural disaster in forested mountainous regions [...]…”
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  18. 18

    Landslide-oriented disaster resilience evaluation in mountainous cities: A case study in Chongqing, China by Huang, Junhao, Wen, Haijia, Li, Zhuohang, Zhang, Yalan

    Published in Resilient Cities and Structures (01-12-2024)
    “…•Developed a novel a framework to assess spatial resilience of mountainous cities to landslides.•Proposed systematic resilience evaluation indicators for…”
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  19. 19

    Analysis of Soil–Water Characteristics and Stability Evolution of Rainfall-Induced Landslide: A Case of the Siwan Village Landslide by Haijia Wen, Jiafeng Xiao, Xiongfeng Wang, Xuekun Xiang, Xinzhi Zhou

    Published in Forests (01-04-2023)
    “…This paper aimed to study the soil–water characteristics and stability evolution law of rainfall-induced landslide. Taking the two landslide events in Siwan…”
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  20. 20

    A prediction model for Xiangyang Neolithic sites based on a random forest algorithm by Li, Linzhi, Chen, Xingyu, Sun, Deliang, Wen, Haijia

    Published in Open Geosciences (31-03-2023)
    “…The archaeological site prediction model can accurately identify archaeological site areas to enable better knowledge and understanding of human civilization…”
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