A landslide susceptibility assessment method using SBAS-InSAR to optimize Bayesian network DOI Creative Commons
Xinyu Gao, Bo Wang, Wen Dai

et al.

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13

Published: Feb. 27, 2025

Landslide susceptibility assessment is crucial to mitigate the severe impacts of landslides. Although Bayesian network (BN) has been widely used in landslide assessment, no study compared accuracy different BN structure construction methods for this purpose. SBAS-InSAR technology plays a vital role research, but its advantages combined with further improve prediction still need be studied. This paper takes Hanyuan County as area. First, 20 traditional impact factors were extracted from data such topography and meteorology. A new method GDSP was designed fuse GeoDetector SHAP dominant factor screening. Then, 8 learning using AUC value ROC curve, among which Tabu&K2 showed highest accuracy. The deformation calculated by then incorporated into model. optimized (OPT-BN) outperformed unoptimized version (ORI-BN) accuracy, mapping more reasonable. reverse inference highlighted that areas lower elevation, plow land, impervious cover, higher rainfall are prone provides valuable insights hazard prevention control future research.

Language: Английский

Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis DOI Creative Commons

A.L. Achu,

C. D. Aju,

Mariano Di Napoli

et al.

Geoscience Frontiers, Journal Year: 2023, Volume and Issue: 14(6), P. 101657 - 101657

Published: June 29, 2023

Landslide susceptibility maps are vital tools used by decision-makers to adopt mitigation strategies for future calamities. In this context, research on landslide modelling has become a topic of relevance and is in constant evolution. Though various machine-learning techniques (MLTs) have been identified modelling, the uncertainty inherent models rarely considered. The present study attempts quantify associated with prediction developing new methodological framework based ensembles eight MLTs. This methodology tested at highlands southern Western Ghats region (Kerala, India), where landslides frequently occurring. Fourteen conditioning factors as part study, their association was correlated 671 historic area. four ensemble such mean probabilities, median weighted committee average. probability proved be best model average 800 standalone MLTs, viz., receiver operating characteristics, true skill statistics, area under curve corresponding validation scores. Thereafter, an analysis carried out coefficient variation. A confident map generated represent distinct zonation areas definite scales. Nearly 74% past fall higher susceptibility-low category. It also inferred that micro-level MLTs may improve efficiency help accurately identifying landslide-prone future. thus can ready reference planners formulation adaptation micro-scales.

Language: Английский

Citations

61
Modelling landslide susceptibility prediction: A review and construction of semi-supervised imbalanced theory DOI
Faming Huang, Haowen Xiong, Shui‐Hua Jiang

et al.

Earth-Science Reviews, Journal Year: 2024, Volume and Issue: 250, P. 104700 - 104700

Published: Jan. 29, 2024

Language: Английский

Citations

61
Advanced integration of ensemble learning and MT-InSAR for enhanced slow-moving landslide susceptibility zoning DOI
Taorui Zeng, Liyang Wu, Yuichi S. Hayakawa

et al.

Engineering Geology, Journal Year: 2024, Volume and Issue: 331, P. 107436 - 107436

Published: Feb. 9, 2024

Language: Английский

Citations

26
Landslide susceptibility assessment using information quantity and machine learning integrated models: a case study of Sichuan province, southwestern China DOI

Pengtao Zhao,

Ying Wang, Yi Xie

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 18, 2025

Language: Английский

Citations

2
Assessing the imperative of conditioning factor grading in machine learning-based landslide susceptibility modeling: A critical inquiry DOI Open Access
Taorui Zeng,

Bijing Jin,

Thomas Glade

et al.

CATENA, Journal Year: 2023, Volume and Issue: 236, P. 107732 - 107732

Published: Dec. 7, 2023

Language: Английский

Citations

40
Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method DOI Creative Commons
Faming Huang,

Zuokui Teng,

Chi Yao

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2023, Volume and Issue: 16(1), P. 213 - 230

Published: Nov. 20, 2023

In the existing landslide susceptibility prediction (LSP) models, influences of random errors in conditioning factors on LSP are not considered, instead original directly taken as model inputs, which brings uncertainties to results. This study aims reveal influence rules different proportional uncertainties, and further explore a method can effectively reduce factors. The firstly used construct factors-based then 5%, 10%, 15% 20% added these for constructing relevant errors-based models. Secondly, low-pass filter-based models constructed by eliminating using filter method. Thirdly, Ruijin County China with 370 landslides 16 case. Three typical machine learning i.e. multilayer perceptron (MLP), support vector (SVM) forest (RF), selected Finally, discussed results show that: (1) decrease uncertainties. (2) With proportions increasing from 5% 20%, uncertainty increases continuously. (3) feasible absence more accurate (4) degrees two issues, errors, modeling large basically same. (5) Shapley values explain internal mechanism predicting susceptibility. conclusion, greater proportion higher uncertainty, errors.

Language: Английский

Citations

31
Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity DOI Creative Commons
Taorui Zeng, Zizheng Guo, Linfeng Wang

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(16), P. 4111 - 4111

Published: Aug. 21, 2023

The expansion of mountainous urban areas and road networks can influence the terrain, vegetation, material characteristics, thereby altering susceptibility landslides. Understanding relationship between human engineering activities landslide occurrence is great significance for both prevention land resource management. In this study, an analysis was conducted on caused by Typhoon Megi in 2016. A representative area along eastern coast China—characterized development, deforestation, severe expansion—was used to analyze spatial distribution For purpose, high-precision Planet optical remote sensing images were obtain inventory related event. main innovative features are as follows: (i) newly developed patch generating land-use simulation (PLUS) model simulated analyzed driving factors land-cover (LULC) from 2010 2060; (ii) stacking strategy combined three strong ensemble models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Machine (LightGBM)—to calculate susceptibility; (iii) distance LULC maps short-term long-term dynamic examine impact susceptibility. results show that maximum built-up 2020 13.433 km2, mainly expanding forest cropland land, with 8.28 km2 5.99 respectively. predicted map 2060 shows a growth 45.88 distributed around government residences relatively flat terrain frequent socio-economic activities. factor contribution has higher than LULC. Stacking RF-XGB-LGBM obtained optimal AUC value 0.915 Furthermore, future network have intensified probability landslides occurring 2015. To our knowledge, first application PLUS models international literature. research serve foundation developing management guidelines reduce risk failures.

Language: Английский

Citations

29
Landslide topology uncovers failure movements DOI Creative Commons
Kushanav Bhuyan, Kamal Rana, Joaquin V. Ferrer

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 25, 2024

Abstract The death toll and monetary damages from landslides continue to rise despite advancements in predictive modeling. These models’ performances are limited as landslide databases used developing them often miss crucial information, e.g., underlying movement types. This study introduces a method of discerning movements, such slides, flows, falls, by analyzing landslides’ 3D shapes. By examining topological properties, we discover distinct patterns their morphology, indicating different movements including complex ones with multiple coupled movements. We achieve 80-94% accuracy applying properties identifying across diverse geographical climatic regions, Italy, the US Pacific Northwest, Denmark, Turkey, Wenchuan China. Furthermore, demonstrate real-world application on undocumented datasets Wenchuan. Our work paradigm for studying shapes understand through lens topology, which could aid models risk evaluations.

Language: Английский

Citations

14
Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scales DOI Creative Commons
Faming Huang, Yu Cao,

Wenbin Li

et al.

International Journal of Coal Science & Technology, Journal Year: 2024, Volume and Issue: 11(1)

Published: April 5, 2024

Abstract This study aims to investigate the effects of different mapping unit scales and area on uncertainty rules landslide susceptibility prediction (LSP). To illustrate various scales, Ganzhou City in China, its eastern region (Ganzhou East), Ruijin County East were chosen. Different are represented by grid units with spatial resolution 30 60 m, as well slope that extracted multi-scale segmentation method. The 3855 locations 21 typical environmental factors first determined create datasets input-outputs. Then, maps (LSMs) City, produced using a support vector machine (SVM) random forest (RF), respectively. LSMs above three regions then mask from LSM along East. Additionally, at generated accordance. Accuracy indexes (LSIs) distribution used express LSP uncertainties. uncertainties under significantly decrease County, whereas those less affected scales. Of course, attentions should also be paid broader representativeness large areas. accuracy increases about 6%–10% compared m same area's scale. significance exhibits an averaging trend scale small large. importance varies greatly unit, but it tends consistent some extent unit. Graphic abstract

Language: Английский

Citations

14
Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review DOI Creative Commons
Stephen Akosah, Ivan Gratchev, Donghyun Kim

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 2947 - 2947

Published: Aug. 12, 2024

This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution research publication trends, (3) progress of algorithms, (4) application techniques models for landslide susceptibility mapping, detections, prediction, inventory deformation monitoring, assessment, extraction management. selections were based on keyword searches using title/abstract keywords from Web Science Scopus. A total 186 articles published between 2011 2024 critically reviewed to provide answers questions related the recent advances use technologies combined with artificial intelligence (AI), machine (ML), deep (DL) algorithms. review revealed that these methods have high efficiency detection, hazard mapping. few current issues also identified discussed.

Language: Английский

Citations

9