Scientific reports

fp essentials

landslides

[1] Combining release and runout in statistical landslide susceptibility modeling.

[2] Numerical simulation of the tsunamis generated by the Sciara del Fuoco landslides (Stromboli Island, Italy).

[3] Disaster Medicine: Public Health Threats Associated With Disasters.

[4] Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

[5] Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

[6] Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis

[7] Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

[8] Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

[9] Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

[10] Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia

[11] Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

[12] Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping

[13] Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques.

[14] Combining release and runout in statistical landslide susceptibility modeling.

[15] Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles.

[16] Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features.

[17] Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping

[18] Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment

[19] Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin

[20] Evaluation of the extreme rainfall predictions and their impact on landslide susceptibility in a sub-catchment scale

[21] Landslide susceptibility mapping by using a geographic information system (GIS) along the China–Pakistan Economic Corridor (Karakoram Highway), Pakistan

[22] Correction to: On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco) (Bulletin of Engineering Geology and the Environment, (2020), 79, 1, (551-568), 10.1007/s10064-019-01548-5)

[23] GIS-based landslide susceptibility mapping using heuristic and bivariate statistical methods for Iva Valley and environs Southeast Nigeria.

[24] A spatially explicit deep learning neural network model for the prediction of landslide susceptibility

[25] Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment

[26] Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble.

[27] Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

[28] Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

[29] Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

[30] Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

[31] Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)

[32] Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran

[33] Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis

[34] A Novel Performance Assessment Approach Using Photogrammetric Techniques for Landslide Susceptibility Mapping with Logistic Regression, ANN and Random Forest

[35] Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

[36] A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

[37] Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models

[38] Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

[39] Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

[40] Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study

[41] Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances

[42] A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

[43] Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

[44] Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

[45] Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

[46] Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

[47] Application of analytic hierarchy process, frequency ratio, and statistical index to landslide susceptibility: an approach to endangered cultural heritage

[48] Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach

[49] Application and cross-validation of spatial logistic multiple regression for landslide susceptibility analysis

[50] A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China

[51] Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

[52] GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region

[53] Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran

[54] Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran

[55] Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

[56] A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate

[57] Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

[58] Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong

[59] Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia

[60] A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

[61] GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey)

[62] Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

[63] Modification of landslide susceptibility mapping using optimized PSO-ANN technique

[64] Statistical analysis of landslide susceptibility at Yongin, Korea

[65] A GIS-Based Multivariate Statistical Analysis for Shallow Landslide Susceptibility Mapping in La Pobla de Lillet Area (Eastern Pyrenees, Spain)

[66] Landslide susceptibility analysis and verification using the Bayesian probability model

[67] An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas

[68] A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping

[69] GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran

[70] implications of landslide typology and predisposing factor combinations for probabilistic landslide susceptibility models: a case study in lajedo parish (flores island, azores—portugal)

[71] landslide susceptibility mapping on a global scale using the method of logistic regression

[72] landslide susceptibility estimation by random forests technique: sensitivity and scaling issues

[73] landslide susceptibility assessment considering landslide typology. a case study in the area north of lisbon (portugal)

[74] Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

[75] Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

[76] An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district

[77] A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

[78] New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed

[79] Assessment of Landslide Susceptibility Based on Multiresolution Image Segmentation and Geological Factor Ratings.

[80] Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy)

[81] Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

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

[83] Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling

[84] GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

[85] Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

[86] Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar–Resadiye (Tokat, Turkey)

[87] Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods

[88] Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling

[89] Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping

[90] Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

[91] Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

[92] GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam

[93] Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia

[94] Shallow landslide susceptibility assessment using a novel hybrid intelligence approach

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

[96] Adaptive Neuro-Fuzzy Inference System (ANFIS) Method for Developing a Decision Support System for Determining Landslide Susceptibility