Results 181 to 190 of about 26,434 (253)

Optimizing machine learning and bagging-based hybrid models for landslide susceptibility mapping: a case study in Chenggu County, China. [PDF]

Sci Rep
Lei X, Liu J, Du Y, Liu L, Yuan Y, Zhao X, Guo Z, Wu X.   +7 more
europepmc   +1 more source

Landslide Susceptibility Mapping by Fusing Convolutional Neural Networks and Vision Transformer. [PDF]

Sensors (Basel), 2022
Bao S, Liu J, Wang L, Konečný M, Che X, Xu S, Li P.   +6 more
europepmc   +1 more source

AHP multi criteria analysis for landslide susceptibility mapping in the Tellian Atlas chain. [PDF]

Sci Rep
Zighmi K, Zahri F, Faqeih K, Al Amri A, Riheb H, Alamri SM, Alamery E.   +6 more
europepmc   +1 more source

Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning. [PDF]

Int J Environ Res Public Health, 2022
Wang X, Zhang X, Bi J, Zhang X, Deng S, Liu Z, Wang L, Guo H.   +7 more
europepmc   +1 more source

Overcoming the data limitations in landslide susceptibility modeling. [PDF]

Sci Adv
Woodard JB, Mirus BB.
europepmc   +1 more source

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India. [PDF]

Heliyon, 2023
Das J, Saha P, Mitra R, Alam A, Kamruzzaman M.   +4 more
europepmc   +1 more source

Evaluation of landslide susceptibility based on SMOTE-Tomek sampling and machine learning algorithm. [PDF]

PLoS One
Lv MZ, Li KL, Cai JZ, Mao J, Gao JJ, Xu H.   +5 more
europepmc   +1 more source

Landslide data sample augmentation and landslide susceptibility analysis in Nyingchi City based on the MCMC model. [PDF]

Sci Rep
Liu L, Wang P, Su L, Li F.
europepmc   +1 more source

Convolutional neural network-based deep learning for landslide susceptibility mapping in the Bakhtegan watershed. [PDF]

Sci Rep
Feng L, Zhang M, Mao Y, Liu H, Yang C, Dong Y, Nanehkaran YA.   +6 more
europepmc   +1 more source

Landslide susceptibility assessment using lightweight dense residual network with emphasis on deep spatial features. [PDF]

Sci Rep
Xu S, Wang Z, Liu J, Ma X, Zhou T, Tang Q.   +5 more
europepmc   +1 more source