805 related articles for article (PubMed ID: 30696105)
1. Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China.
Wang Y; Wu X; Chen Z; Ren F; Feng L; Du Q
Int J Environ Res Public Health; 2019 Jan; 16(3):. PubMed ID: 30696105
[TBL] [Abstract][Full Text] [Related]
2. Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China).
Wang Y; Sun D; Wen H; Zhang H; Zhang F
Int J Environ Res Public Health; 2020 Jun; 17(12):. PubMed ID: 32545618
[TBL] [Abstract][Full Text] [Related]
3. Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors.
Luo X; Lin F; Zhu S; Yu M; Zhang Z; Meng L; Peng J
PLoS One; 2019; 14(4):e0215134. PubMed ID: 30973936
[TBL] [Abstract][Full Text] [Related]
4. GIS-based landslide susceptibility mapping in the Longmen Mountain area (China) using three different machine learning algorithms and their comparison.
Huang Z; Peng L; Li S; Liu Y; Zhou S
Environ Sci Pollut Res Int; 2023 Aug; 30(38):88612-88626. PubMed ID: 37440134
[TBL] [Abstract][Full Text] [Related]
5. Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China.
Zhou X; Wu W; Lin Z; Zhang G; Chen R; Song Y; Wang Z; Lang T; Qin Y; Ou P; Huangfu W; Zhang Y; Xie L; Huang X; Fu X; Li J; Jiang J; Zhang M; Liu Y; Peng S; Shao C; Bai Y; Zhang X; Liu X; Liu W
Int J Environ Res Public Health; 2021 May; 18(11):. PubMed ID: 34072874
[TBL] [Abstract][Full Text] [Related]
6. Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models.
Tengtrairat N; Woo WL; Parathai P; Aryupong C; Jitsangiam P; Rinchumphu D
Sensors (Basel); 2021 Jul; 21(13):. PubMed ID: 34283153
[TBL] [Abstract][Full Text] [Related]
7. A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran.
Shen Y; Ahmadi Dehrashid A; Bahar RA; Moayedi H; Nasrollahizadeh B
Environ Sci Pollut Res Int; 2023 Dec; 30(59):123527-123555. PubMed ID: 37987977
[TBL] [Abstract][Full Text] [Related]
8. Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity.
Jin B; Zeng T; Yin K; Gui L; Guo Z; Wang T
Environ Sci Pollut Res Int; 2024 Jan; 31(5):7872-7888. PubMed ID: 38170358
[TBL] [Abstract][Full Text] [Related]
9. A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran.
Ghasemian B; Shahabi H; Shirzadi A; Al-Ansari N; Jaafari A; Kress VR; Geertsema M; Renoud S; Ahmad A
Sensors (Basel); 2022 Feb; 22(4):. PubMed ID: 35214473
[TBL] [Abstract][Full Text] [Related]
10. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China.
Chen W; Peng J; Hong H; Shahabi H; Pradhan B; Liu J; Zhu AX; Pei X; Duan Z
Sci Total Environ; 2018 Jun; 626():1121-1135. PubMed ID: 29898519
[TBL] [Abstract][Full Text] [Related]
11. Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China.
Xiao B; Zhao J; Li D; Zhao Z; Zhou D; Xi W; Li Y
Sensors (Basel); 2022 Oct; 22(20):. PubMed ID: 36298394
[TBL] [Abstract][Full Text] [Related]
12. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms.
Nhu VH; Shirzadi A; Shahabi H; Singh SK; Al-Ansari N; Clague JJ; Jaafari A; Chen W; Miraki S; Dou J; Luu C; Górski K; Thai Pham B; Nguyen HD; Ahmad BB
Int J Environ Res Public Health; 2020 Apr; 17(8):. PubMed ID: 32316191
[TBL] [Abstract][Full Text] [Related]
13. Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea.
Hakim WL; Rezaie F; Nur AS; Panahi M; Khosravi K; Lee CW; Lee S
J Environ Manage; 2022 Mar; 305():114367. PubMed ID: 34968941
[TBL] [Abstract][Full Text] [Related]
14. Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture.
Li Y; Deng X; Ji P; Yang Y; Jiang W; Zhao Z
Int J Environ Res Public Health; 2022 Oct; 19(21):. PubMed ID: 36361126
[TBL] [Abstract][Full Text] [Related]
15. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan.
Dou J; Yunus AP; Tien Bui D; Merghadi A; Sahana M; Zhu Z; Chen CW; Khosravi K; Yang Y; Pham BT
Sci Total Environ; 2019 Apr; 662():332-346. PubMed ID: 30690368
[TBL] [Abstract][Full Text] [Related]
16. Evaluation of Different Landslide Susceptibility Models for a Local Scale in the Chitral District, Northern Pakistan.
Aslam B; Maqsoom A; Khalil U; Ghorbanzadeh O; Blaschke T; Farooq D; Tufail RF; Suhail SA; Ghamisi P
Sensors (Basel); 2022 Apr; 22(9):. PubMed ID: 35590797
[TBL] [Abstract][Full Text] [Related]
17. Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda.
Kuradusenge M; Kumaran S; Zennaro M
Int J Environ Res Public Health; 2020 Jun; 17(11):. PubMed ID: 32532022
[TBL] [Abstract][Full Text] [Related]
18. Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping.
Hong H; Tsangaratos P; Ilia I; Loupasakis C; Wang Y
Sci Total Environ; 2020 Nov; 742():140549. PubMed ID: 32629264
[TBL] [Abstract][Full Text] [Related]
19. Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling.
Zhang T; Han L; Chen W; Shahabi H
Entropy (Basel); 2018 Nov; 20(11):. PubMed ID: 33266608
[TBL] [Abstract][Full Text] [Related]
20. Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology.
Saha A; Tripathi L; Villuri VGK; Bhardwaj A
Environ Sci Pollut Res Int; 2024 Feb; 31(7):10443-10459. PubMed ID: 38198087
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]