265 related articles for article (PubMed ID: 35655489)
1. Landslide Susceptibility Evaluation Using Different Slope Units Based on BP Neural Network.
Huang J; Zeng X; Ding L; Yin Y; Li Y
Comput Intell Neurosci; 2022; 2022():9923775. PubMed ID: 35655489
[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. Slide type landslide susceptibility assessment of the Büyük Menderes watershed using artificial neural network method.
Tekin S; Çan T
Environ Sci Pollut Res Int; 2022 Jul; 29(31):47174-47188. PubMed ID: 35178630
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study.
Li L; Lan H
Int J Environ Res Public Health; 2020 Nov; 17(21):. PubMed ID: 33139639
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility mapping.
Moayedi H; Dehrashid AA
Environ Sci Pollut Res Int; 2023 Jul; 30(34):82964-82989. PubMed ID: 37336850
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. 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]
11. Determination of landslide susceptibility with Analytic Hierarchy Process (AHP) and the role of forest ecosystem services on landslide susceptibility.
Aksoy H
Environ Monit Assess; 2023 Nov; 195(12):1525. PubMed ID: 37994954
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya.
Das S; Sarkar S; Kanungo DP
Environ Monit Assess; 2022 Mar; 194(3):234. PubMed ID: 35229227
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey).
Karsli F; Atasoy M; Yalcin A; Reis S; Demir O; Gokceoglu C
Environ Monit Assess; 2009 Sep; 156(1-4):241-55. PubMed ID: 18780152
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China.
Cao Y; Wei X; Fan W; Nan Y; Xiong W; Zhang S
PLoS One; 2021; 16(1):e0245668. PubMed ID: 33493200
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
