361 related articles for article (PubMed ID: 32532022)
1. 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]
2. 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]
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. 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]
5. An ensemble learning-based experimental framework for smart landslide detection, monitoring, prediction, and warning in IoT-cloud environment.
Sharma A; Mohana R; Kukkar A; Chodha V; Bansal P
Environ Sci Pollut Res Int; 2023 Dec; 30(58):122677-122699. PubMed ID: 37971588
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Research on landslide susceptibility prediction model based on LSTM-RF-MDBN.
Yang X; Fan X; Wang K; Zhou Z
Environ Sci Pollut Res Int; 2024 Jan; 31(1):1504-1516. PubMed ID: 38041734
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province.
Kang J; Wan B; Gao Z; Zhou S; Chen H; Shen H
Sci Rep; 2024 Jun; 14(1):14049. PubMed ID: 38890498
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda.
Nsengiyumva JB; Luo G; Nahayo L; Huang X; Cai P
Int J Environ Res Public Health; 2018 Jan; 15(2):. PubMed ID: 29385096
[TBL] [Abstract][Full Text] [Related]
12. Hybrid machine learning approach for landslide prediction, Uttarakhand, India.
Kainthura P; Sharma N
Sci Rep; 2022 Nov; 12(1):20101. PubMed ID: 36418362
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. 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]
16. 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]
17. 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]
18. Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir.
Yang B; Xiao T; Wang L; Huang W
Sensors (Basel); 2022 Feb; 22(4):. PubMed ID: 35214220
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]