196 related articles for article (PubMed ID: 35590797)
1. 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]
2. 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]
3. Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP).
Nath NK; Gautam VK; Pande CB; Mishra LR; Raju JT; Moharir KN; Rane NL
Environ Sci Pollut Res Int; 2024 Jan; 31(5):7481-7497. PubMed ID: 38159190
[TBL] [Abstract][Full Text] [Related]
4. GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh.
Chowdhury MS; Rahman MN; Sheikh MS; Sayeid MA; Mahmud KH; Hafsa B
Heliyon; 2024 Jan; 10(1):e23424. PubMed ID: 38163149
[TBL] [Abstract][Full Text] [Related]
5. Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan.
Dou J; Tien Bui D; Yunus AP; Jia K; Song X; Revhaug I; Xia H; Zhu Z
PLoS One; 2015; 10(7):e0133262. PubMed ID: 26214691
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China).
Zhu Z; Gan S; Yuan X; Zhang J
Sensors (Basel); 2022 Jul; 22(15):. PubMed ID: 35898090
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping.
Feizizadeh B; Shadman Roodposhti M; Jankowski P; Blaschke T
Comput Geosci; 2014 Dec; 73():208-221. PubMed ID: 26089577
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. GIS-based landslide susceptibility mapping using heuristic and bivariate statistical methods for Iva Valley and environs Southeast Nigeria.
Ozioko OH; Igwe O
Environ Monit Assess; 2020 Jan; 192(2):119. PubMed ID: 31950278
[TBL] [Abstract][Full Text] [Related]
13. Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by Persistent Scatterer In-SAR Technique.
Hussain MA; Chen Z; Zheng Y; Shoaib M; Shah SU; Ali N; Afzal Z
Sensors (Basel); 2022 Apr; 22(9):. PubMed ID: 35590807
[TBL] [Abstract][Full Text] [Related]
14. The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China.
Ma Z; Qin S; Cao C; Lv J; Li G; Qiao S; Hu X
Entropy (Basel); 2019 Apr; 21(4):. PubMed ID: 33267086
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping.
Feizizadeh B; Blaschke T
Int J Geogr Inf Sci; 2014 Mar; 28(3):610-638. PubMed ID: 27019609
[TBL] [Abstract][Full Text] [Related]
17. A landslide susceptibility map based on spatial scale segmentation: A case study at Zigui-Badong in the Three Gorges Reservoir Area, China.
Yu X; Gao H
PLoS One; 2020; 15(3):e0229818. PubMed ID: 32160206
[TBL] [Abstract][Full Text] [Related]
18. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.
Yu X; Wang Y; Niu R; Hu Y
Int J Environ Res Public Health; 2016 May; 13(5):. PubMed ID: 27187430
[TBL] [Abstract][Full Text] [Related]
19. Integrating stratified best-worst method and GIS for landslide susceptibility assessment: a case study in Erzurum province (Turkey).
Konurhan Z; Yucesan M; Gul M
Environ Sci Pollut Res Int; 2023 Nov; 30(53):113978-114000. PubMed ID: 37858024
[TBL] [Abstract][Full Text] [Related]
20. Landslide susceptibility mapping by integrating analytical hierarchy process, frequency ratio, and fuzzy gamma operator models, case study: North of Lorestan Province, Iran.
Eitvandi N; Sarikhani R; Derikvand S
Environ Monit Assess; 2022 Jul; 194(9):600. PubMed ID: 35864313
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
