423 related articles for article (PubMed ID: 30690368)
1. 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]
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. 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]
4. 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]
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. 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]
7. Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa.
Nsengiyumva JB; Luo G; Amanambu AC; Mind'je R; Habiyaremye G; Karamage F; Ochege FU; Mupenzi C
Sci Total Environ; 2019 Apr; 659():1457-1472. PubMed ID: 31096356
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment.
Nhu VH; Mohammadi A; Shahabi H; Ahmad BB; Al-Ansari N; Shirzadi A; Clague JJ; Jaafari A; Chen W; Nguyen H
Int J Environ Res Public Health; 2020 Jul; 17(14):. PubMed ID: 32650595
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. 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]
13. 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]
14. 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]
15. Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling.
He Q; Xu Z; Li S; Li R; Zhang S; Wang N; Pham BT; Chen W
Entropy (Basel); 2019 Jan; 21(2):. PubMed ID: 33266822
[TBL] [Abstract][Full Text] [Related]
16. Landslide Susceptibility Evaluation of Machine Learning Based on Information Volume and Frequency Ratio: A Case Study of Weixin County, China.
He W; Chen G; Zhao J; Lin Y; Qin B; Yao W; Cao Q
Sensors (Basel); 2023 Feb; 23(5):. PubMed ID: 36904752
[TBL] [Abstract][Full Text] [Related]
17. Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling.
Chen W; Zhang S; Li R; Shahabi H
Sci Total Environ; 2018 Dec; 644():1006-1018. PubMed ID: 30743814
[TBL] [Abstract][Full Text] [Related]
18. Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway.
Xiao L; Zhang Y; Peng G
Sensors (Basel); 2018 Dec; 18(12):. PubMed ID: 30558225
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. A novel landslide susceptibility optimization framework to assess landslide occurrence probability at the regional scale for environmental management.
Sun X; Yuan L; Tao S; Liu M; Li D; Zhou Y; Shao H
J Environ Manage; 2022 Nov; 322():116108. PubMed ID: 36063695
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]