172 related articles for article (PubMed ID: 38082044)
1. Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia's mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis.
Alqadhi S; Mallick J; Hang HT; Al Asmari AFS; Kumari R
Environ Sci Pollut Res Int; 2024 Jan; 31(2):3169-3194. PubMed ID: 38082044
[TBL] [Abstract][Full Text] [Related]
2. Game-theoretic optimization of landslide susceptibility mapping: a comparative study between Bayesian-optimized basic neural network and new generation neural network models.
Mallick J; Alkahtani M; Hang HT; Singh CK
Environ Sci Pollut Res Int; 2024 Apr; 31(20):29811-29835. PubMed ID: 38592629
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia.
Alqadhi S; Mallick J; Talukdar S; Bindajam AA; Van Hong N; Saha TK
Environ Sci Pollut Res Int; 2022 Jan; 29(3):3743-3762. PubMed ID: 34389958
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
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. Deep learning-based landslide susceptibility mapping.
Azarafza M; Azarafza M; Akgün H; Atkinson PM; Derakhshani R
Sci Rep; 2021 Dec; 11(1):24112. PubMed ID: 34916586
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. 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]
12. 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]
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. 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]
15. 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]
16. Landslide susceptibility assessment based on frequency ratio and semi-supervised heterogeneous ensemble learning model.
Zhao Y; Qin S; Zhang C; Yao J; Xing Z; Cao J; Zhang R
Environ Sci Pollut Res Int; 2024 May; 31(22):32043-32059. PubMed ID: 38642229
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. 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]
20. 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]
[Next] [New Search]
