On the public citation graph

Review on landslide susceptibility mapping using support vector machines

https://doi.org/10.1016/j.catena.2018.03.003

0 signed · 796 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-48412-4_35openalex-snapshot

Pump Failure Detection Using Support Vector Data Descriptions

inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot

The Nature of Statistical Learning Theory

inferredcites →https://doi.org/10.1007/978-81-322-1041-2_24openalex-snapshot

An Improved Bayesian Classification Data Mining Method for Early Warning Landslide Susceptibility Model Using GIS

inferredcites →https://doi.org/10.1007/s10064-013-0538-8openalex-snapshot

Recommendations for the quantitative analysis of landslide risk

inferredcites →https://doi.org/10.1007/s10064-015-0786-xopenalex-snapshot

GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China

inferredcites →https://doi.org/10.1007/s10064-015-0804-zopenalex-snapshot

Prediction of landslide displacement based on GA-LSSVM with multiple factors

inferredcites →https://doi.org/10.1007/s10346-013-0391-7openalex-snapshot

Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression

inferredcites →https://doi.org/10.1007/s10346-014-0547-0openalex-snapshot

Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping

inferredcites →https://doi.org/10.1007/s10346-014-0550-5openalex-snapshot

A systematic review of landslide probability mapping using logistic regression

inferredcites →https://doi.org/10.1007/s10346-015-0557-6openalex-snapshot

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

inferredcites →https://doi.org/10.1007/s10346-016-0711-9openalex-snapshot

Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization

inferredcites →https://doi.org/10.1007/s10346-016-0771-xopenalex-snapshot

A modified frequency ratio method for landslide susceptibility assessment

inferredcites →https://doi.org/10.1007/s10706-016-9990-0openalex-snapshot

A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area

inferredcites →https://doi.org/10.1007/s11004-011-9379-9openalex-snapshot

Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy

inferredcites →https://doi.org/10.1007/s11004-013-9511-0openalex-snapshot

Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

inferredcites →https://doi.org/10.1007/s11069-016-2304-2openalex-snapshot

Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS

inferredcites →https://doi.org/10.1007/s11431-010-3219-xopenalex-snapshot

A new method of pseudo absence data generation in landslide susceptibility mapping with a case study of Shenzhen

inferredcites →https://doi.org/10.1007/s12040-013-0282-2openalex-snapshot

Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran

inferredcites →https://doi.org/10.1007/s12665-009-0394-9openalex-snapshot

Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine

inferredcites →https://doi.org/10.1007/s12665-013-2863-4openalex-snapshot

Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China

inferredcites →https://doi.org/10.1007/s12665-014-3758-8openalex-snapshot

Interactive statistical analysis of predisposing factors for earthquake-triggered landslides: a case study of the 2013 Lushan, China Ms7.0 earthquake

inferredcites →https://doi.org/10.1007/s12665-016-5576-7openalex-snapshot

Soft computing in assessment of earthquake-triggered landslide susceptibility

inferredcites →https://doi.org/10.1007/s12665-016-6162-8openalex-snapshot

Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions

inferredcites →https://doi.org/10.1007/s12665-017-6689-3openalex-snapshot

A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS

inferredcites →https://doi.org/10.1016/0022-2496(77)90033-5openalex-snapshot

A scaling method for priorities in hierarchical structures

inferredcites →https://doi.org/10.1016/0377-2217(85)90321-2openalex-snapshot

Classification and regression trees

inferredcites →https://doi.org/10.1016/j.cageo.2008.08.007openalex-snapshot

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

inferredcites →https://doi.org/10.1016/j.cageo.2012.01.002openalex-snapshot

Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China

inferredcites →https://doi.org/10.1016/j.cageo.2012.08.023openalex-snapshot

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

inferredcites →https://doi.org/10.1016/j.cageo.2014.08.001openalex-snapshot

A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping

inferredcites →https://doi.org/10.1016/j.cageo.2015.04.007openalex-snapshot

Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

inferredcites →https://doi.org/10.1016/j.catena.2014.02.005openalex-snapshot

Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia

inferredcites →https://doi.org/10.1016/j.catena.2015.05.019openalex-snapshot

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

inferredcites →https://doi.org/10.1016/j.enggeo.2009.12.004openalex-snapshot

Techniques for evaluating the performance of landslide susceptibility models

inferredcites →https://doi.org/10.1016/j.enggeo.2011.09.006openalex-snapshot

Landslide susceptibility assessment using SVM machine learning algorithm

inferredcites →https://doi.org/10.1016/j.enggeo.2016.02.009openalex-snapshot

Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China

inferredcites →https://doi.org/10.1016/j.envsoft.2009.10.016openalex-snapshot

Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling

inferredcites →https://doi.org/10.1016/j.envsoft.2012.09.011openalex-snapshot

Characterising performance of environmental models

inferredcites →https://doi.org/10.1016/j.envsoft.2016.07.005openalex-snapshot

A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)

inferredcites →https://doi.org/10.1016/j.geomorph.2004.06.010openalex-snapshot

The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan

inferredcites →https://doi.org/10.1016/j.geomorph.2006.12.011openalex-snapshot

Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression

inferredcites →https://doi.org/10.1016/j.geomorph.2008.02.011openalex-snapshot

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

inferredcites →https://doi.org/10.1016/j.geomorph.2010.01.003openalex-snapshot

Landslides induced by the 2008 Wenchuan earthquake, Sichuan, China

inferredcites →https://doi.org/10.1016/j.geomorph.2010.12.030openalex-snapshot

Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake

inferredcites →https://doi.org/10.1016/j.geomorph.2011.12.040openalex-snapshot

GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China

inferredcites →https://doi.org/10.1016/j.geomorph.2013.08.013openalex-snapshot

Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China

inferredcites →https://doi.org/10.1016/j.geomorph.2013.09.012openalex-snapshot

Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method

inferredcites →https://doi.org/10.1016/j.jag.2013.09.010openalex-snapshot

An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: The Candir catchment area (western Antalya, Turkey)

inferredcites →https://doi.org/10.1016/j.jhydrol.2014.03.008openalex-snapshot

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

inferredcites →https://doi.org/10.1016/j.rse.2014.05.013openalex-snapshot

Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale

inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot

Random Forests

inferredcites →https://doi.org/10.1023/b:nhaz.0000007172.62651.2bopenalex-snapshot

Validation of Spatial Prediction Models for Landslide Hazard Mapping

inferredcites →https://doi.org/10.1080/00401706.1996.10484565openalex-snapshot

The Nature of Statistical Learning Theory

inferredcites →https://doi.org/10.1080/0143116031000114851openalex-snapshot

The use of backpropagating artificial neural networks in land cover classification

inferredcites →https://doi.org/10.1080/01431161.2013.774099openalex-snapshot

An assessment of the effectiveness of a rotation forest ensemble for land-use and land-cover mapping

inferredcites →https://doi.org/10.1109/72.788641openalex-snapshot

Input space versus feature space in kernel-based methods

inferredcites →https://doi.org/10.1109/iri.2003.1251420openalex-snapshot

Automatic parameter selection for polynomial kernel

inferredcites →https://doi.org/10.1109/jstars.2014.2308553openalex-snapshot

Susceptibility Assessment of Landslides Triggered by the Lushan Earthquake, April 20, 2013, China

inferredcites →https://doi.org/10.1109/mcsa.2003.1240761openalex-snapshot

Proceedings Fifth IEEE Workshop on Mobile Computing Systems and Applications

inferredcites →https://doi.org/10.1109/tkde.2010.99openalex-snapshot

Missing Value Estimation for Mixed-Attribute Data Sets

inferredcites →https://doi.org/10.1126/science.aad8728openalex-snapshot

Global trends in satellite-based emergency mapping

inferredcites →https://doi.org/10.1130/g33217.1openalex-snapshot

Global patterns of loss of life from landslides

inferredcites →https://doi.org/10.1155/2012/974638openalex-snapshot

Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models

inferredcites →https://doi.org/10.1162/089976603321891855openalex-snapshot

Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

inferredcites →https://doi.org/10.1371/journal.pone.0017191openalex-snapshot

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

inferredcites →https://doi.org/10.1680/jensu.16.00045openalex-snapshot

Resilience and sustainability in the management of landslides

inferredcites →https://doi.org/10.2307/2530946openalex-snapshot

Classification and Regression Trees.

inferredcites →https://doi.org/10.5194/gmd-7-1247-2014openalex-snapshot

Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

inferredcites →https://doi.org/10.5194/nhess-5-853-2005openalex-snapshot

Spatial prediction models for landslide hazards: review, comparison and evaluation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4685102openalex-extracted

Scale Coupling Enables More Detailed Mapping of Landslide Susceptibility

inferred← citeshttps://doi.org/10.1002/9781394287222.ch7openalex-snapshot

AI‐Based Landslide Susceptibility Evaluation

inferred← citeshttps://doi.org/10.1002/esp.5064openalex-snapshot

Landslide susceptibility analysis based on citizen reports

inferred← citeshttps://doi.org/10.1002/esp.5748openalex-snapshot

Insights into landslide susceptibility in different karst erosion landforms based on interpretable machine learning

inferred← citeshttps://doi.org/10.1002/esp.6032openalex-snapshot

Machine learning for surficial geologic mapping

inferred← citeshttps://doi.org/10.1002/ett.3998openalex-snapshot

Review on remote sensing methods for landslide detection using machine and deep learning

inferred← citeshttps://doi.org/10.1002/gj.4666openalex-snapshot

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

inferred← citeshttps://doi.org/10.1002/gj.4936openalex-snapshot

Landslide susceptibility prediction modelling based on semi‐supervised XGBoost model

inferred← citeshttps://doi.org/10.1002/gj.70372openalex-snapshot

Enhancing Landslide Susceptibility Assessment With a Novel IV ‐ LR ‐ RF Coupled Model: A Comparative Study in Lvhe Town, Qinling‐Daba Mountains, China

inferred← citeshttps://doi.org/10.1002/gj.70455openalex-snapshot

Machine Learning‐Based Landslide Hazard and Risk Assessment in the Darma Valley, NW Himalaya

inferred← citeshttps://doi.org/10.1002/hyp.13821openalex-snapshot

Establishing a threshold for rainfall‐induced landslides by a kinetic energy–duration relationship

inferred← citeshttps://doi.org/10.1007/978-3-030-60319-9_36openalex-snapshot

Extreme Rainfall Induced Landslide Susceptibility Assessment Using Autoencoder Combined with Random Forest

inferred← citeshttps://doi.org/10.1007/978-3-030-85928-2_16openalex-snapshot

Temporal Attention-Based Graph Convolution Network for Taxi Demand Prediction in Functional Areas

inferred← citeshttps://doi.org/10.1007/978-3-031-11898-2_17openalex-snapshot

Seismic Landslide Susceptibility Assessment Based on Seismic Ground Motion and Earthquake Disaster Analysis

inferred← citeshttps://doi.org/10.1007/978-3-031-15377-8_14openalex-snapshot

Landslide Susceptibility Evaluation and Analysis: A Review on Articles Published During 2000 to 2020

inferred← citeshttps://doi.org/10.1007/978-3-031-20667-2_16openalex-snapshot

Landslide Susceptibility Mapping Using Supervised Learning Methods – Case Study: Southwestern Colombia

inferred← citeshttps://doi.org/10.1007/978-3-031-26580-8_7openalex-snapshot

Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning

inferred← citeshttps://doi.org/10.1007/978-3-031-55120-8_11openalex-snapshot

Evaluation of Assessment Models for Landslide Susceptibility Mapping in Permafrost Areas

inferred← citeshttps://doi.org/10.1007/978-3-031-56591-5_6openalex-snapshot

Towards Artificially Intelligent Landslide Susceptibility Mapping: A Critical Review and Open Questions

inferred← citeshttps://doi.org/10.1007/978-3-031-72736-8_13openalex-snapshot

The Accuracy of Landslide Susceptibility Mapping in Young Glacial River Valleys

inferred← citeshttps://doi.org/10.1007/978-3-031-82194-3_7openalex-snapshot

Machine Learning Techniques in Hydrogeological Research

inferred← citeshttps://doi.org/10.1007/978-3-031-95083-4_13openalex-snapshot

Landslide Inventory in Parts of Sikkim, Eastern Indian Himalaya: Navigating Spatial and Temporal Patterns

inferred← citeshttps://doi.org/10.1007/978-3-032-25176-3_18openalex-snapshot

An Optimized Boosting Algorithm-Based Meta Learning Ensemble for Landslide Susceptibility Prediction: A Study in South Sikkim

inferred← citeshttps://doi.org/10.1007/978-3-032-39470-5_13openalex-snapshot

Landslide Susceptibility Mapping in Nam Pam Commune, Vietnam, Using the OpenLSM Tool

inferred← citeshttps://doi.org/10.1007/978-981-16-3783-4_3openalex-snapshot

Predicting Transmission Rate of Coronavirus (COVID-19) Pandemic Using Machine Learning Techniques

inferred← citeshttps://doi.org/10.1007/978-981-19-6597-5_14openalex-snapshot

Earthquake-Induced Landslide Susceptibility and Hazard Assessment Approaches

inferred← citeshttps://doi.org/10.1007/978-981-92-0835-7_9openalex-snapshot

Evaluating Conditioning Factors Influencing Terrain Stability in Himalayan Intermountain Regions

inferred← citeshttps://doi.org/10.1007/978-981-95-9485-6_7openalex-snapshot

GIS-Based Landslide Susceptibility Zonation of Kohima District Using the Frequency Ratio Model

inferred← citeshttps://doi.org/10.1007/978-981-99-7707-9_1openalex-snapshot

Landslide Susceptibility Assessment Based on Machine Learning Techniques

inferred← citeshttps://doi.org/10.1007/s00024-022-03152-3openalex-snapshot

Probabilistic Analysis of the Landslide Hazard in Cold Regions: Considering Multiple Triggering Factors and Their Interdependence

inferred← citeshttps://doi.org/10.1007/s00477-019-01696-wopenalex-snapshot

Spatial prediction of landslide susceptibility in Taleghan basin, Iran

inferred← citeshttps://doi.org/10.1007/s00477-021-02032-xopenalex-snapshot

Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China

inferred← citeshttps://doi.org/10.1007/s00477-022-02208-zopenalex-snapshot

Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction

inferred← citeshttps://doi.org/10.1007/s00477-023-02556-4openalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s00477-024-02841-wopenalex-snapshot

Comparative study of sampling strategies for machine learning-based landslide susceptibility assessment

inferred← citeshttps://doi.org/10.1007/s00477-026-03284-1openalex-snapshot

Landslide susceptibility assessment in Huizhou, South China: a comparison between 3-dimensional convolutional neural network and traditional point-based models

inferred← citeshttps://doi.org/10.1007/s10064-020-01863-2openalex-snapshot

A novel landslide susceptibility mapping portrayed by OA-HD and K-medoids clustering algorithms

inferred← citeshttps://doi.org/10.1007/s10064-020-01969-7openalex-snapshot

Comparative landslide spatial research based on various sample sizes and ratios in Penang Island, Malaysia

inferred← citeshttps://doi.org/10.1007/s10064-022-02672-5openalex-snapshot

The uncertainty of landslide susceptibility prediction modeling: suitability of linear conditioning factors

inferred← citeshttps://doi.org/10.1007/s10064-022-02748-2openalex-snapshot

Landslide susceptibility prediction using an incremental learning Bayesian Network model considering the continuously updated landslide inventories

inferred← citeshttps://doi.org/10.1007/s10064-022-02806-9openalex-snapshot

Landslide susceptibility zonation around Dharamshala, Himachal Pradesh, India: an artificial intelligence model–based assessment

inferred← citeshttps://doi.org/10.1007/s10064-022-02821-wopenalex-snapshot

Characteristics and susceptibility assessment of the earthquake-triggered landslides in moderate-minor earthquake prone areas at southern margin of Sichuan Basin, China

inferred← citeshttps://doi.org/10.1007/s10064-022-02836-3openalex-snapshot

A frequency ratio–based sampling strategy for landslide susceptibility assessment

inferred← citeshttps://doi.org/10.1007/s10064-022-02843-4openalex-snapshot

Ground fissure susceptibility mapping based on factor optimization and support vector machines

inferred← citeshttps://doi.org/10.1007/s10064-023-03230-3openalex-snapshot

Prediction of landslide susceptibility in Wenchuan County based on pixel-level samples

inferred← citeshttps://doi.org/10.1007/s10064-023-03344-8openalex-snapshot

GIS based landslide susceptibility zonation mapping using frequency ratio, information value and weight of evidence: a case study in Kinnaur District HP India

inferred← citeshttps://doi.org/10.1007/s10064-023-03408-9openalex-snapshot

Comparative analysis of five convolutional neural networks for landslide susceptibility assessment

inferred← citeshttps://doi.org/10.1007/s10064-023-03409-8openalex-snapshot

Assessing landslide susceptibility based on hybrid multilayer perceptron with ensemble learning

inferred← citeshttps://doi.org/10.1007/s10064-023-03440-9openalex-snapshot

A quick method of early landslide identification based on dynamic susceptibility analysis using M-SVM method: a case study

inferred← citeshttps://doi.org/10.1007/s10064-024-03548-6openalex-snapshot

Active waveguide deformation dynamics using acoustic emission technology for landslide early warning system

inferred← citeshttps://doi.org/10.1007/s10064-024-03687-wopenalex-snapshot

Susceptibility assessment of earthquake-induced landslide by using back-propagation neural network in the Southwest mountainous area of China

inferred← citeshttps://doi.org/10.1007/s10064-024-03712-yopenalex-snapshot

Landslide susceptibility mapping and sensitivity analysis using various machine learning models: a case study of Beas valley, Indian Himalaya

inferred← citeshttps://doi.org/10.1007/s10064-024-03724-8openalex-snapshot

Post-earthquake spatiotemporal evolution characteristics of typical landslide sources in the Jiuzhaigou meizoseismal area

inferred← citeshttps://doi.org/10.1007/s10064-024-03859-8openalex-snapshot

Improving generalization performance of landslide susceptibility model considering spatial heterogeneity by using the geomorphic label-based LightGBM

inferred← citeshttps://doi.org/10.1007/s10064-024-03879-4openalex-snapshot

Analyzing spatial non-stationarity effects of driving factors on landslides: a multiscale geographically weighted regression approach based on slope units

inferred← citeshttps://doi.org/10.1007/s10064-024-04008-xopenalex-snapshot

An improved buffer-controlled sampling strategy for landslide susceptibility assessment considering the spatial heterogeneity of conditioning factors

inferred← citeshttps://doi.org/10.1007/s10064-024-04026-9openalex-snapshot

Failure mechanism of toppling in anti-dip layered rock slope: a case study of the Xiangpingshan landslide in southwest China

inferred← citeshttps://doi.org/10.1007/s10064-025-04147-9openalex-snapshot

Influences of non-landslide sampling strategies on landslide susceptibility mapping: a case of Tianshui city, Northwest of China

inferred← citeshttps://doi.org/10.1007/s10064-025-04180-8openalex-snapshot

Evaluating the uncertainty in landslide susceptibility prediction: effect of spatial data variability and evaluation unit choices

inferred← citeshttps://doi.org/10.1007/s10064-025-04182-6openalex-snapshot

Study on movement characteristics of landslide considering earthquake magnitude and duration time

inferred← citeshttps://doi.org/10.1007/s10064-025-04281-4openalex-snapshot

Effects of different division methods of landslide susceptibility levels on regional landslide susceptibility mapping

inferred← citeshttps://doi.org/10.1007/s10064-025-04490-xopenalex-snapshot

Susceptibility assessment and driving factor analysis of geological hazards in complex landform areas: insights from sichuan, China

inferred← citeshttps://doi.org/10.1007/s10064-025-04682-5openalex-snapshot

Machine learning-based global landslide susceptibility analysis: spatiotemporal variability and dominant environmental associations

inferred← citeshttps://doi.org/10.1007/s10064-026-04796-4openalex-snapshot

Landslide susceptibility assessment under non-landslide sampling strategy based on a clustering algorithm

inferred← citeshttps://doi.org/10.1007/s10064-026-04863-wopenalex-snapshot

Influence of sample purity on landslide susceptibility mapping: a mutually exclusive stratified purity strategy based on the state-process consistency

inferred← citeshttps://doi.org/10.1007/s10064-026-04905-3openalex-snapshot

A deep learning multi-branch network method for landslide susceptibility mapping in the Sichuan-Tibet transportation corridor

inferred← citeshttps://doi.org/10.1007/s10064-026-04911-5openalex-snapshot

Validation and prediction challenges in data-driven landslide susceptibility mapping: insights from random sampling of training and test data in machine learning models

inferred← citeshttps://doi.org/10.1007/s10064-026-04917-zopenalex-snapshot

Statistical–machine learning coupled models predicting landslide susceptibility in Liangshan Yi Autonomous Prefecture: performance comparison and factor explanations

inferred← citeshttps://doi.org/10.1007/s10064-026-04940-0openalex-snapshot

Integrating machine learning and deep learning for enhanced landslide susceptibility and propagation dynamics in Northwestern Pakistan

inferred← citeshttps://doi.org/10.1007/s10064-026-05069-wopenalex-snapshot

Evaluation of landslide susceptibility in alpine canyon area based on random forest method: a case study of southeast Tibetan Plateau

inferred← citeshttps://doi.org/10.1007/s10064-026-05172-yopenalex-snapshot

Enhancing landslide susceptibility assessment through ensemble learning and PCoA

inferred← citeshttps://doi.org/10.1007/s10064-026-05296-1openalex-snapshot

Multi-level InSAR coupling and spatially-probabilistic constrained imbalanced sampling for refined landslide susceptibility assessment in active tectonic orogens

inferred← citeshttps://doi.org/10.1007/s10346-020-01444-0openalex-snapshot

Dynamic development of landslide susceptibility based on slope unit and deep neural networks

inferred← citeshttps://doi.org/10.1007/s10346-020-01473-9openalex-snapshot

Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model

inferred← citeshttps://doi.org/10.1007/s10346-021-01756-9openalex-snapshot

Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments

inferred← citeshttps://doi.org/10.1007/s10346-021-01796-1openalex-snapshot

Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area

inferred← citeshttps://doi.org/10.1007/s10346-022-02020-4openalex-snapshot

Land use and land cover as a conditioning factor in landslide susceptibility: a literature review

inferred← citeshttps://doi.org/10.1007/s10346-023-02027-5openalex-snapshot

Community perceptions of landslide risk and susceptibility: a multi-country study

inferred← citeshttps://doi.org/10.1007/s10346-023-02045-3openalex-snapshot

Risk assessment of roadway networks exposed to landslides in mountainous regions—a case study in Fengjie County, China

inferred← citeshttps://doi.org/10.1007/s10346-023-02089-5openalex-snapshot

A dual-encoder U-Net for landslide detection using Sentinel-2 and DEM data

inferred← citeshttps://doi.org/10.1007/s10346-023-02152-1openalex-snapshot

An ensemble of dynamic rainfall index and machine learning method for spatiotemporal landslide susceptibility modeling

inferred← citeshttps://doi.org/10.1007/s10346-024-02213-zopenalex-snapshot

A small landslide induced a large disaster prior to the heavy rainy season in Jinkouhe, Sichuan, China: characteristics, mechanism, and lessons

inferred← citeshttps://doi.org/10.1007/s10462-024-10836-wopenalex-snapshot

State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

inferred← citeshttps://doi.org/10.1007/s10668-024-04694-3openalex-snapshot

Improving the forecast performance of landslide susceptibility mapping by using ensemble gradient boosting algorithms

inferred← citeshttps://doi.org/10.1007/s10706-025-03330-9openalex-snapshot

GIS-Based Multi-Criteria Analysis for Landslide Susceptibility Mapping in Pulwama, Jammu and Kashmir

inferred← citeshttps://doi.org/10.1007/s10706-025-03424-4openalex-snapshot

Deep Learning in Slope Stability Analysis: Evolution, Challenges, and Future Directions

inferred← citeshttps://doi.org/10.1007/s10712-020-09609-1openalex-snapshot

Remote Sensing for Assessing Landslides and Associated Hazards

inferred← citeshttps://doi.org/10.1007/s11004-023-10044-2openalex-snapshot

Enriching Representation and Enhancing Nearest Neighbor Classification of Slope/Landslide Data Using Rectified Feature Line Segments and Hypersphere-Based Scaling: A Reproducible Experimental Comparison

inferred← citeshttps://doi.org/10.1007/s11004-023-10116-3openalex-snapshot

Automated Machine Learning-Based Landslide Susceptibility Mapping for the Three Gorges Reservoir Area, China

inferred← citeshttps://doi.org/10.1007/s11004-024-10168-zopenalex-snapshot

Towards a Synergistic Progressive Ensemble Framework for Automatic Post-Earthquake Landslide Recognition and Susceptibility Assessment

inferred← citeshttps://doi.org/10.1007/s11004-026-10305-wopenalex-snapshot

Two-Stage Automatic-Transfer Geological Mapping Method Based on Multisource Data Fusion

inferred← citeshttps://doi.org/10.1007/s11042-022-13638-wopenalex-snapshot

Moderate embed cross validated and feature reduced Steganalysis using principal component analysis in spatial and transform domain with Support Vector Machine and Support Vector Machine-Particle Swarm Optimization

inferred← citeshttps://doi.org/10.1007/s11069-019-03595-3openalex-snapshot

Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China

inferred← citeshttps://doi.org/10.1007/s11069-019-03785-zopenalex-snapshot

Assessment of land subsidence susceptibility in Semnan plain (Iran): a comparison of support vector machine and weights of evidence data mining algorithms

inferred← citeshttps://doi.org/10.1007/s11069-020-03899-9openalex-snapshot

The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China

inferred← citeshttps://doi.org/10.1007/s11069-020-03927-8openalex-snapshot

Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China

inferred← citeshttps://doi.org/10.1007/s11069-020-04337-6openalex-snapshot

A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide

inferred← citeshttps://doi.org/10.1007/s11069-021-04688-8openalex-snapshot

A review of landslides related to the 2005 Kashmir Earthquake: implication and future challenges

inferred← citeshttps://doi.org/10.1007/s11069-021-04719-4openalex-snapshot

Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics

inferred← citeshttps://doi.org/10.1007/s11069-021-04743-4openalex-snapshot

Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey

inferred← citeshttps://doi.org/10.1007/s11069-021-04812-8openalex-snapshot

Assessment of landslide susceptibility and risk factors in China

inferred← citeshttps://doi.org/10.1007/s11069-021-04823-5openalex-snapshot

Remote sensing GIS-based landslide susceptibility & risk modeling in Darjeeling–Sikkim Himalaya together with FEM-based slope stability analysis of the terrain

inferred← citeshttps://doi.org/10.1007/s11069-021-04844-0openalex-snapshot

A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping

inferred← citeshttps://doi.org/10.1007/s11069-021-04986-1openalex-snapshot

Bagging-based machine learning algorithms for landslide susceptibility modeling

inferred← citeshttps://doi.org/10.1007/s11069-021-05083-zopenalex-snapshot

Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy

inferred← citeshttps://doi.org/10.1007/s11069-022-05325-8openalex-snapshot

Spatial prediction of highway slope disasters based on convolution neural networks

inferred← citeshttps://doi.org/10.1007/s11069-022-05329-4openalex-snapshot

Discussion on the tree-based machine learning model in the study of landslide susceptibility

inferred← citeshttps://doi.org/10.1007/s11069-022-05357-0openalex-snapshot

How do multiple kernel functions in machine learning algorithms improve precision in flood probability mapping?

inferred← citeshttps://doi.org/10.1007/s11069-022-05360-5openalex-snapshot

An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India

inferred← citeshttps://doi.org/10.1007/s11069-022-05588-1openalex-snapshot

Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning

inferred← citeshttps://doi.org/10.1007/s11069-022-05603-5openalex-snapshot

Identification of landslide-prone zones using a GIS-based multi-criteria decision analysis and region-growing algorithm in uncertain conditions

inferred← citeshttps://doi.org/10.1007/s11069-022-05671-7openalex-snapshot

Landslide hazard and susceptibility maps derived from satellite and remote sensing data using limit equilibrium analysis and machine learning model

inferred← citeshttps://doi.org/10.1007/s11069-022-05764-3openalex-snapshot

Landslide susceptibility prediction mapping with advanced ensemble models: Son La province, Vietnam

inferred← citeshttps://doi.org/10.1007/s11069-023-05878-2openalex-snapshot

Badland erosion susceptibility mapping using machine learning data mining techniques, Firozkuh watershed, Iran

inferred← citeshttps://doi.org/10.1007/s11069-023-06092-wopenalex-snapshot

Influence of landslide inventory timespan and data selection on slope unit-based susceptibility models

inferred← citeshttps://doi.org/10.1007/s11069-023-06260-yopenalex-snapshot

Integrated deep learning with explainable artificial intelligence for enhanced landslide management

inferred← citeshttps://doi.org/10.1007/s11069-023-06306-1openalex-snapshot

Evaluating landslide susceptibility: an AHP method-based approach enhanced with optimized random forest modeling

inferred← citeshttps://doi.org/10.1007/s11069-024-06405-7openalex-snapshot

Prediction of rainfall-induced landslide using machine learning models along highway Bandipora to Gurez road, India

inferred← citeshttps://doi.org/10.1007/s11069-024-06465-9openalex-snapshot

Seismic vulnerability assessment model of civil structure using machine learning algorithms: a case study of the 2014 Ms6.5 Ludian earthquake

inferred← citeshttps://doi.org/10.1007/s11069-024-06521-4openalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s11069-024-06576-3openalex-snapshot

The influence of cartographic representation on landslide susceptibility models: empirical evidence from a Brazilian UNESCO world heritage site

inferred← citeshttps://doi.org/10.1007/s11069-024-06602-4openalex-snapshot

An information quantity and machine learning integrated model for landslide susceptibility mapping in Jiuzhaigou, China

inferred← citeshttps://doi.org/10.1007/s11069-024-06649-3openalex-snapshot

GIS-based fuzzy logic technique for mapping landslide susceptibility analyzing in a coastal soft rock zone

inferred← citeshttps://doi.org/10.1007/s11069-024-06665-3openalex-snapshot

A support vector machine model of landslide susceptibility mapping based on hyperparameter optimization using the Bayesian algorithm: a case study of the highways in the southern Qinghai–Tibet Plateau

inferred← citeshttps://doi.org/10.1007/s11069-024-06673-3openalex-snapshot

Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence

inferred← citeshttps://doi.org/10.1007/s11069-024-06697-9openalex-snapshot

Landslide susceptibility mapping core-base factors and models’ performance variability: a systematic review

inferred← citeshttps://doi.org/10.1007/s11069-025-07579-4openalex-snapshot

Physics-based landslide susceptibility machine learning model for mountainous solar power plants

inferred← citeshttps://doi.org/10.1007/s11069-025-07924-7openalex-snapshot

Research on the application of stacking ensemble learning model with negative sample constraints in landslide susceptibility assessment

inferred← citeshttps://doi.org/10.1007/s11069-025-07928-3openalex-snapshot

Structure-driven evaluation of GBDT and neural networks for Geo-Tabular landslide hazard assessment on the Tibetan plateau

inferred← citeshttps://doi.org/10.1007/s11069-026-08265-9openalex-snapshot

DRCD-TL: a novel co-seismic landslide mapping approach by deep representation-driven unsupervised change detection based on transfer learning

inferred← citeshttps://doi.org/10.1007/s11069-026-08316-1openalex-snapshot

Artificial intelligence for remote sensing-based detection and prediction of landslides in Malaysia and Vietnam: A state-of-the-art review

inferred← citeshttps://doi.org/10.1007/s11356-022-21931-2openalex-snapshot

Utilization of frequency ratio method for the production of landslide susceptibility maps: Karaburun Peninsula case, Turkey

inferred← citeshttps://doi.org/10.1007/s11356-022-23732-zopenalex-snapshot

A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020

inferred← citeshttps://doi.org/10.1007/s11356-023-25454-2openalex-snapshot

The influence of the selection of non-geological disasters sample spatial range on the evaluation of environmental geological disasters susceptibility: a case study of Liulin County

inferred← citeshttps://doi.org/10.1007/s11356-023-28966-zopenalex-snapshot

Evaluating causative factors for landslide susceptibility along the Imphal-Jiribam railway corridor in the North-Eastern part of India using a GIS-based statistical approach

inferred← citeshttps://doi.org/10.1007/s11356-023-31352-4openalex-snapshot

Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia’s mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis

inferred← citeshttps://doi.org/10.1007/s11356-023-31688-xopenalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s11356-023-31739-3openalex-snapshot

Influence of buffer distance on environmental geological hazard susceptibility assessment

inferred← citeshttps://doi.org/10.1007/s11356-024-33287-wopenalex-snapshot

Landslide susceptibility assessment based on frequency ratio and semi-supervised heterogeneous ensemble learning model

inferred← citeshttps://doi.org/10.1007/s11356-024-34134-8openalex-snapshot

Balancing aquaculture and estuarine ecosystems: machine learning–based water quality indices for effective management

inferred← citeshttps://doi.org/10.1007/s11440-023-01841-4openalex-snapshot

Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales

inferred← citeshttps://doi.org/10.1007/s11554-021-01105-yopenalex-snapshot

The role of film and television big data in real-time image detection and processing in the Internet of Things era

inferred← citeshttps://doi.org/10.1007/s11600-022-00767-xopenalex-snapshot

GIS-based landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan using genetic algorithm-based ensemble models

inferred← citeshttps://doi.org/10.1007/s11629-018-5337-zopenalex-snapshot

GIS-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine

inferred← citeshttps://doi.org/10.1007/s11629-019-5702-6openalex-snapshot

Optimizing the frequency ratio method for landslide susceptibility assessment: A case study of the Caiyuan Basin in the southeast mountainous area of China

inferred← citeshttps://doi.org/10.1007/s11629-020-6277-yopenalex-snapshot

Comparative performance assessment of landslide susceptibility models with presence-only, presence-absence, and pseudo-absence data

inferred← citeshttps://doi.org/10.1007/s11629-020-6491-7openalex-snapshot

Assessment of landslide susceptibility using DBSCAN-AHD and LD-EV methods

inferred← citeshttps://doi.org/10.1007/s11629-022-7370-1openalex-snapshot

An hourly shallow landslide warning model developed by combining automatic landslide spatial susceptibility and temporal rainfall threshold predictions

inferred← citeshttps://doi.org/10.1007/s11629-022-7527-yopenalex-snapshot

Effects of the probability of pulse-like ground motions on landslide susceptibility assessment in near-fault areas

inferred← citeshttps://doi.org/10.1007/s11629-022-7698-6openalex-snapshot

Application of logistic regression model for hazard assessment of landslides caused by the 2012 Yiliang Ms 5.7 earthquake in Yunnan Province, China

inferred← citeshttps://doi.org/10.1007/s11629-025-0006-5openalex-snapshot

Research progress in landslide susceptibility assessment driven by machine learning: A bibliometric analysis

inferred← citeshttps://doi.org/10.1007/s11629-025-0204-1openalex-snapshot

Post-earthquake landslide hazard evolution: Spatio-temporal analysis of active fault zone in Western Himalayas

inferred← citeshttps://doi.org/10.1007/s11629-025-9556-9openalex-snapshot

Multi-scale risk assessment method for pipeline slope hazards

inferred← citeshttps://doi.org/10.1007/s11629-025-9813-yopenalex-snapshot

Assessing urban expansion into landslide susceptibility zones using machine learning methods: A case study of Yunnan Province, China

inferred← citeshttps://doi.org/10.1007/s12040-024-02344-wopenalex-snapshot

Landslide susceptibility zonation using integrated supervised and unsupervised machine learning techniques in the Bhagirathi Eco-Sensitive Zone (BESZ), Uttarakhand, Himalaya, India

inferred← citeshttps://doi.org/10.1007/s12040-025-02665-4openalex-snapshot

A predictive, GIS-based, landslide susceptibility study of Tamenglong district, Manipur, India using the frequency ratio approach: A case study

inferred← citeshttps://doi.org/10.1007/s12040-025-02676-1openalex-snapshot

Geospatial based prediction of landslide prone areas and stability assessment of Bhabanagar and Tapri landslides in district Kinnaur, Himachal Pradesh, India

inferred← citeshttps://doi.org/10.1007/s12046-024-02652-3openalex-snapshot

Seismic landslide susceptibility assessment based on game theory combination weighting and information volume model: a case study of Luding earthquake high-intensity area

inferred← citeshttps://doi.org/10.1007/s12046-025-03025-0openalex-snapshot

Landslide hazard mapping of Darjeeling using GIS and ensemble machine learning model

inferred← citeshttps://doi.org/10.1007/s12145-019-00389-wopenalex-snapshot

Mapping landslide susceptibility in the Zagros Mountains, Iran: a comparative study of different data mining models

inferred← citeshttps://doi.org/10.1007/s12145-019-00429-5openalex-snapshot

Parsimonious design of pattern recognition systems for slope stability analysis

inferred← citeshttps://doi.org/10.1007/s12145-022-00826-3openalex-snapshot

Data Integration for Lithological Mapping Using Machine Learning Algorithms

inferred← citeshttps://doi.org/10.1007/s12145-022-00889-2openalex-snapshot

Landslide identification using machine learning techniques: Review, motivation, and future prospects

inferred← citeshttps://doi.org/10.1007/s12145-023-01151-zopenalex-snapshot

Optimization of negative sample selection for landslide susceptibility mapping based on machine learning using K-means-KNN algorithm

inferred← citeshttps://doi.org/10.1007/s12145-024-01229-2openalex-snapshot

Incremental learning-random forest model-based landslide susceptibility analysis: A case of Ganzhou City, China

inferred← citeshttps://doi.org/10.1007/s12145-024-01561-7openalex-snapshot

Exploring uncertainty analysis in GIS-based Landslide susceptibility mapping models using machine learning in the Darjeeling Himalayas

inferred← citeshttps://doi.org/10.1007/s12145-025-01700-8openalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s12145-025-01727-xopenalex-snapshot

Optimizing landslide susceptibility mapping using integrated forest by penalizing attributes model with ensemble algorithms

inferred← citeshttps://doi.org/10.1007/s12145-025-01753-9openalex-snapshot

High-precision landslide susceptibility assessment based on the coupling of IHAOAVOA algorithm and BP neural network

inferred← citeshttps://doi.org/10.1007/s12145-025-01766-4openalex-snapshot

Indispensable factors in landslide susceptibility modeling: the critical role of slope unit quantity-sensitivity

inferred← citeshttps://doi.org/10.1007/s12517-021-06630-5openalex-snapshot

A comparative study of the bivariate, multivariate and machine-learning-based statistical models for landslide susceptibility mapping in a seismic-prone region in China

inferred← citeshttps://doi.org/10.1007/s12517-021-08871-wopenalex-snapshot

Landslide susceptibility mapping using GIS-based machine learning algorithms for the Northeast Chongqing Area, China

inferred← citeshttps://doi.org/10.1007/s12517-022-09672-5openalex-snapshot

Application of analytical hierarchal process, frequency ratio, and Shannon entropy approaches for landslide susceptibility mapping using geospatial technology: The case of Dejen district, Ethiopia

inferred← citeshttps://doi.org/10.1007/s12517-023-11541-8openalex-snapshot

Modelling landslides in the Lesser Himalaya region using geospatial and numerical simulation techniques

inferred← citeshttps://doi.org/10.1007/s12517-023-11627-3openalex-snapshot

Comparison study of a landslide-event hazard mapping using a multi-approach of fuzzy logic, TRIGRS model, and support vector machine in a data-scarce Andes Mountain region

inferred← citeshttps://doi.org/10.1007/s12524-024-01953-8openalex-snapshot

Creation of a Landslide Inventory for the 2018 Storm Event of Kodagu in the Western Ghats for Landslide Susceptibility Mapping Using Machine Learning

inferred← citeshttps://doi.org/10.1007/s12524-025-02356-zopenalex-snapshot

GIS-based Landslide Susceptibility Model for North Kerala Using Logistics Regression and Machine Learning Approaches

inferred← citeshttps://doi.org/10.1007/s12583-020-1072-9openalex-snapshot

An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping: Application in Two Areas of Three Gorges Reservoir, China

inferred← citeshttps://doi.org/10.1007/s12583-022-1644-yopenalex-snapshot

Susceptibility Mapping of Ground Collapse Caused by Anthropogenic Activities

inferred← citeshttps://doi.org/10.1007/s12652-024-04764-4openalex-snapshot

Machine learning for physical motion identification using EEG signals: a comparative study of classifiers and hyperparameter tuning

inferred← citeshttps://doi.org/10.1007/s12665-019-8415-9openalex-snapshot

Landslide susceptibility evaluating using artificial intelligence method in the Youfang district (China)

inferred← citeshttps://doi.org/10.1007/s12665-019-8562-zopenalex-snapshot

A comparative study of support vector machine and logistic model tree classifiers for shallow landslide susceptibility modeling

inferred← citeshttps://doi.org/10.1007/s12665-021-09525-6openalex-snapshot

An approach based on socio-politically optimized neural computing network for predicting shallow landslide susceptibility at tropical areas

inferred← citeshttps://doi.org/10.1007/s12665-021-09650-2openalex-snapshot

Landslide susceptibility mapping with GIS in high mountain area of Nepal: a comparison of four methods

inferred← citeshttps://doi.org/10.1007/s12665-021-09696-2openalex-snapshot

A variable weight combination model for prediction on landslide displacement using AR model, LSTM model, and SVM model: a case study of the Xinming landslide in China

inferred← citeshttps://doi.org/10.1007/s12665-021-09896-wopenalex-snapshot

Comparison of wave-cluster and DBSCAN algorithms for landslide susceptibility assessment

inferred← citeshttps://doi.org/10.1007/s12665-021-10152-4openalex-snapshot

A comparison study on the quantitative statistical methods for spatial prediction of shallow landslides (case study: Yozidar-Degaga Route in Kurdistan Province, Iran)

inferred← citeshttps://doi.org/10.1007/s12665-022-10375-zopenalex-snapshot

Evaluation of landslide susceptibility of the Ya’an–Linzhi section of the Sichuan–Tibet Railway based on deep learning

inferred← citeshttps://doi.org/10.1007/s12665-022-10470-1openalex-snapshot

Landslide susceptibility mapping with the integration of information theory, fractal theory, and statistical analyses at a regional scale: a case study of Altay Prefecture, China

inferred← citeshttps://doi.org/10.1007/s12665-022-10603-6openalex-snapshot

Application of artificial neural networks and geographic information system to provide hazard susceptibility maps for rockfall failures

inferred← citeshttps://doi.org/10.1007/s12665-023-10803-8openalex-snapshot

Impact of orthogonal transformation for factors on model performance in landslide susceptibility

inferred← citeshttps://doi.org/10.1007/s12665-023-10844-zopenalex-snapshot

Exploring performance and robustness of shallow landslide susceptibility modeling at regional scale using different training and testing sets

inferred← citeshttps://doi.org/10.1007/s12665-024-11501-9openalex-snapshot

Study on the influence of input variables on the supervised machine learning model for landslide susceptibility mapping

inferred← citeshttps://doi.org/10.1007/s12665-024-11521-5openalex-snapshot

Synergizing multiple machine learning techniques and remote sensing for advanced landslide susceptibility assessment: a case study in the Three Gorges Reservoir Area

inferred← citeshttps://doi.org/10.1007/s12665-025-12148-wopenalex-snapshot

Effect of different mapping units, spatial resolutions, and machine learning algorithms on landslide susceptibility mapping at the township scale

inferred← citeshttps://doi.org/10.1007/s12665-025-12660-zopenalex-snapshot

Analysis of the impact of landslide susceptibility on critical infrastructure in large urban agglomerations: using interpretable boosting models and critical infrastructure spatial index

inferred← citeshttps://doi.org/10.1007/s12665-026-12840-5openalex-snapshot

Interpretable susceptibility mapping of loess landslides integrating soil parameters using machine learning and SHAP: a case study from the loess plateau in western Shanxi, China

inferred← citeshttps://doi.org/10.1007/s12665-026-13057-2openalex-snapshot

Risk assessment of landslide and collapse disasters in mining area of Lishi using ensemble learning

inferred← citeshttps://doi.org/10.1007/s13042-019-01028-yopenalex-snapshot

Bibliometric analysis of support vector machines research trend: a case study in China

inferred← citeshttps://doi.org/10.1007/s13762-021-03572-zopenalex-snapshot

GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China

showing the first 200 of 727 · page through all 727

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.