A Novel Heterogeneous Ensemble Framework Based on Machine Learning Models for Shallow Landslide Susceptibility Mapping DOI Creative Commons
Haozhe Tang, Changming Wang,

Silong An

и другие.

Remote Sensing, Год журнала: 2023, Номер 15(17), С. 4159 - 4159

Опубликована: Авг. 24, 2023

Landslides are devastating natural disasters that seriously threaten human life and property. Landslide susceptibility mapping (LSM) plays a key role in landslide hazard management. Machine learning (ML) models widely used LSM but suffer from limitations such as overfitting unreliable accuracy. To improve the classification performance of single machine model, this study selects logistic regression (LR), support vector (SVM), random forest (RF), gradient boosting decision tree (GBDT), proposes novel heterogeneous ensemble framework based on Bayesian optimization (BO), namely, stratified weighted averaging (SWA), to test its applicability typical area Yanbian Prefecture, China. Firstly, dataset consisting 1531 historical landslides was collected field investigations records, spatial database containing 16 predisposing factors established. The divided into training set ratio 7:3. results showed SWA effectively improved Accuracy, AUC, robustness model compared ML model. achieved best (Accuracy = 91.39% AUC 0.967). verify generalization ability SWA, we selected published datasets Yanshan country Yongxin China for testing. also performed well, with an 0.871 0.860, respectively. As indicated by shapely values (SVs), Normalized Difference Vegetation Index (NDVI) is factor has greatest impact occurrence. maps obtained will provide effective reference program land use planning disaster prevention mitigation projects

Язык: Английский

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

и другие.

Engineering Geology, Год журнала: 2024, Номер 331, С. 107436 - 107436

Опубликована: Фев. 9, 2024

Язык: Английский

Процитировано

25
Optimizing landslide susceptibility mapping using machine learning and geospatial techniques DOI Creative Commons

Gazali Agboola,

Leila Hashemi-Beni, Tamer Elbayoumi

и другие.

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102583 - 102583

Опубликована: Март 30, 2024

Landslides present a substantial risk to human lives, the environment, and infrastructure. Consequently, it is crucial highlight regions prone future landslides by examining correlation between past various geo-environmental factors. This study aims investigate optimal data selection machine learning model, or ensemble technique, for evaluating vulnerability of areas determining most accurate approach. To attain our objectives, we considered two different scenarios selecting landslide-free random points (a slope threshold buffer-based approach) performed comparative analysis five models landslide susceptibility mapping, namely: Support Vector Machine (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Random Forest (RF), Extreme Gradient Boosting (XGBoost). The area this research an in Polk County Western North Carolina that has experienced fatal landslides, leading casualties significant damage infrastructure, properties, road networks. model construction process involves utilization dataset comprising 1215 historical occurrences non-landslide points. We integrated total fourteen geospatial layers, consisting topographic variables, soil data, geological land cover attributes. use metrics assess models' performance, including accuracy, F1-score, Kappa score, AUC-ROC. In addition, used seeded-cell index (SCAI) evaluate map consistency. using Weighted Average produces outstanding results, with AUC-ROC 99.4% scenario 91.8% scenario. Our findings emphasize impact sampling on performance mapping. Furthermore, optimally identifying landslide-prone hotspots need urgent management planning, demonstrates effectiveness analyzing providing valuable insights informed decision-making disaster reduction initiatives.

Язык: Английский

Процитировано

25
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

и другие.

CATENA, Год журнала: 2023, Номер 236, С. 107732 - 107732

Опубликована: Дек. 7, 2023

Язык: Английский

Процитировано

39
Deep Learning and Machine Learning Models for Landslide Susceptibility Mapping with Remote Sensing Data DOI Creative Commons

Muhammad Afaq Hussain,

Zhanlong Chen,

Ying Zheng

и другие.

Remote Sensing, Год журнала: 2023, Номер 15(19), С. 4703 - 4703

Опубликована: Сен. 26, 2023

Karakoram Highway (KKH) is an international route connecting South Asia with Central and China that holds socio-economic strategic significance. However, KKH has extreme geological conditions make it prone vulnerable to natural disasters, primarily landslides, posing a threat its routine activities. In this context, the study provides updated inventory of landslides in area precisely measured slope deformation (Vslope), utilizing SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) PS-InSAR (persistent scatterer technology. By processing Sentinel-1 data from June 2021 2023, InSAR technique, total 571 were identified classified based on government reports field investigations. A 24 new prospective identified, some existing redefined. This landslide was then utilized create susceptibility model, which investigated link between occurrences causal variables. Deep learning (DL) machine (ML) models, including convolutional neural networks (CNN 2D), recurrent (RNNs), random forest (RF), gradient boosting (XGBoost), are employed. The split into 70% for training 30% testing fifteen causative factors used mapping. To compare accuracy under curve (AUC) receiver operating characteristic (ROC) used. CNN 2D technique demonstrated superior performance creating map (LSM) KKH. enhanced LSM modeling approach hazard prevention serves as conceptual reference management risk assessment mitigation.

Язык: Английский

Процитировано

29
Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity DOI Creative Commons
Taorui Zeng, Zizheng Guo, Linfeng Wang

и другие.

Remote Sensing, Год журнала: 2023, Номер 15(16), С. 4111 - 4111

Опубликована: Авг. 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.

Язык: Английский

Процитировано

28
Deep learning powered long-term warning systems for reservoir landslides DOI
Taorui Zeng, Thomas Glade,

Yangyi Xie

и другие.

International Journal of Disaster Risk Reduction, Год журнала: 2023, Номер 94, С. 103820 - 103820

Опубликована: Июнь 25, 2023

Язык: Английский

Процитировано

26
GIS-based landslide susceptibility mapping of Western Rwanda: an integrated artificial neural network, frequency ratio, and Shannon entropy approach DOI
Vincent E. Nwazelibe, Johnbosco C. Egbueri, Chinanu O. Unigwe

и другие.

Environmental Earth Sciences, Год журнала: 2023, Номер 82(19)

Опубликована: Авг. 31, 2023

Язык: Английский

Процитировано

25
Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scales DOI Creative Commons
Faming Huang, Yu Cao,

Wenbin Li

и другие.

International Journal of Coal Science & Technology, Год журнала: 2024, Номер 11(1)

Опубликована: Апрель 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

Язык: Английский

Процитировано

12
Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity DOI

Bijing Jin,

Taorui Zeng, Kunlong Yin

и другие.

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(5), С. 7872 - 7888

Опубликована: Янв. 3, 2024

Язык: Английский

Процитировано

10
Comparative analysis of the TabNet algorithm and traditional machine learning algorithms for landslide susceptibility assessment in the Wanzhou Region of China DOI

Song Yingze,

Song Yingxu,

Xin Zhang

и другие.

Natural Hazards, Год журнала: 2024, Номер 120(8), С. 7627 - 7652

Опубликована: Март 15, 2024

Язык: Английский

Процитировано

10