These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

143 related articles for article (PubMed ID: 38163149)

  • 1. 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]  

  • 2. Predictive landslide susceptibility modeling in the southeastern hilly region of Bangladesh: application of machine learning algorithms in Khagrachari district.
    Hasan MM; Roy SK; Talha MD; Ferdous MT; Nasher NMR
    Environ Sci Pollut Res Int; 2024 Sep; ():. PubMed ID: 39302581
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. Improving ML-based landslide susceptibility using ensemble method for sample selection: a case study of Kangra district in Himachal Pradesh, India.
    Singh A; Dhiman N; K C N; Shukla DP
    Environ Sci Pollut Res Int; 2024 Sep; ():. PubMed ID: 39223412
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. 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]  

  • 17. A review on landslide susceptibility mapping research in Bangladesh.
    Chowdhury MS
    Heliyon; 2023 Jul; 9(7):e17972. PubMed ID: 37519718
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India.
    Das J; Saha P; Mitra R; Alam A; Kamruzzaman M
    Heliyon; 2023 May; 9(5):e16186. PubMed ID: 37234665
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.