BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 33266933)

  • 1. Assessment of Landslide Susceptibility Using Integrated Ensemble Fractal Dimension with Kernel Logistic Regression Model.
    Zhang T; Han L; Han J; Li X; Zhang H; Wang H
    Entropy (Basel); 2019 Feb; 21(2):. PubMed ID: 33266933
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment.
    Shahabi H; Hashim M
    Sci Rep; 2015 Apr; 5():9899. PubMed ID: 25898919
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles.
    Moayedi H; Osouli A; Tien Bui D; Foong LK
    Sensors (Basel); 2019 Oct; 19(21):. PubMed ID: 31671801
    [TBL] [Abstract][Full Text] [Related]  

  • 12. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models.
    Chen W; Li H; Hou E; Wang S; Wang G; Panahi M; Li T; Peng T; Guo C; Niu C; Xiao L; Wang J; Xie X; Ahmad BB
    Sci Total Environ; 2018 Sep; 634():853-867. PubMed ID: 29653429
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 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. Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey).
    Akgun A; Kıncal C; Pradhan B
    Environ Monit Assess; 2012 Sep; 184(9):5453-70. PubMed ID: 21915598
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS.
    Golkarian A; Naghibi SA; Kalantar B; Pradhan B
    Environ Monit Assess; 2018 Feb; 190(3):149. PubMed ID: 29455381
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Mapping landslide susceptibility using data-driven methods.
    Zêzere JL; Pereira S; Melo R; Oliveira SC; Garcia RAC
    Sci Total Environ; 2017 Jul; 589():250-267. PubMed ID: 28262363
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 9.