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 *

131 related articles for article (PubMed ID: 32737384)

  • 1. Development of novel hybridized models for urban flood susceptibility mapping.
    Rahmati O; Darabi H; Panahi M; Kalantari Z; Naghibi SA; Ferreira CSS; Kornejady A; Karimidastenaei Z; Mohammadi F; Stefanidis S; Tien Bui D; Haghighi AT
    Sci Rep; 2020 Jul; 10(1):12937. PubMed ID: 32737384
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms.
    Panahi M; Gayen A; Pourghasemi HR; Rezaie F; Lee S
    Sci Total Environ; 2020 Nov; 741():139937. PubMed ID: 32574917
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A new approach based on biology-inspired metaheuristic algorithms in combination with random forest to enhance the flood susceptibility mapping.
    Razavi-Termeh SV; Sadeghi-Niaraki A; Choi SM
    J Environ Manage; 2023 Nov; 345():118790. PubMed ID: 37647734
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Measurement and analysis of regional flood disaster resilience based on a support vector regression model refined by the selfish herd optimizer with elite opposition-based learning.
    Liu D; Wang C; Ji Y; Fu Q; Li M; Ali S; Li T; Cui S
    J Environ Manage; 2021 Dec; 300():113764. PubMed ID: 34547576
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS.
    Nguyen HD; Nguyen QH; Bui QT
    Environ Sci Pollut Res Int; 2024 Mar; 31(12):18701-18722. PubMed ID: 38349496
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration.
    Tikhamarine Y; Malik A; Souag-Gamane D; Kisi O
    Environ Sci Pollut Res Int; 2020 Aug; 27(24):30001-30019. PubMed ID: 32445152
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm.
    Wang Y; Hong H; Chen W; Li S; Panahi M; Khosravi K; Shirzadi A; Shahabi H; Panahi S; Costache R
    J Environ Manage; 2019 Oct; 247():712-729. PubMed ID: 31279803
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum.
    Zhang M; Kong P; Xia A; Tuo W; Lv Y; Wang S
    Biomimetics (Basel); 2023 Mar; 8(2):. PubMed ID: 37092384
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction.
    Adnan MSG; Siam ZS; Kabir I; Kabir Z; Ahmed MR; Hassan QK; Rahman RM; Dewan A
    J Environ Manage; 2023 Jan; 326(Pt B):116813. PubMed ID: 36435143
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Integrated machine learning methods with resampling algorithms for flood susceptibility prediction.
    Dodangeh E; Choubin B; Eigdir AN; Nabipour N; Panahi M; Shamshirband S; Mosavi A
    Sci Total Environ; 2020 Feb; 705():135983. PubMed ID: 31841902
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping.
    Paryani S; Neshat A; Pourghasemi HR; Ntona MM; Kazakis N
    Sci Total Environ; 2022 Feb; 807(Pt 3):151055. PubMed ID: 34673066
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Spatial mapping of land susceptibility to dust emissions using optimization of attentive Interpretable Tabular Learning (TabNet) model.
    Razavi-Termeh SV; Sadeghi-Niaraki A; Sorooshian A; Abuhmed T; Choi SM
    J Environ Manage; 2024 May; 358():120682. PubMed ID: 38670008
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India.
    Arora A; Arabameri A; Pandey M; Siddiqui MA; Shukla UK; Bui DT; Mishra VN; Bhardwaj A
    Sci Total Environ; 2021 Jan; 750():141565. PubMed ID: 32882492
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches.
    Widya LK; Rezaie F; Lee W; Lee CW; Nurwatik N; Lee S
    J Environ Manage; 2024 Jul; 364():121291. PubMed ID: 38875975
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings.
    Ngo NT; Truong TTH; Truong NS; Pham AD; Huynh NT; Pham TM; Pham VHS
    Sci Rep; 2022 Jan; 12(1):1065. PubMed ID: 35058495
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution.
    Hong H; Panahi M; Shirzadi A; Ma T; Liu J; Zhu AX; Chen W; Kougias I; Kazakis N
    Sci Total Environ; 2018 Apr; 621():1124-1141. PubMed ID: 29074239
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Flood susceptibility evaluation through deep learning optimizer ensembles and GIS techniques.
    Costache R; Arabameri A; Costache I; Crăciun A; Md Towfiqul Islam AR; Abba SI; Sahana M; Pham BT
    J Environ Manage; 2022 Aug; 316():115316. PubMed ID: 35598454
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms.
    Razavi Termeh SV; Kornejady A; Pourghasemi HR; Keesstra S
    Sci Total Environ; 2018 Feb; 615():438-451. PubMed ID: 28988080
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Parameter estimation of Muskingum model using grey wolf optimizer algorithm.
    Akbari R; Hessami-Kermani MR
    MethodsX; 2021; 8():101589. PubMed ID: 35004221
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Coupling machine learning with signal process techniques and particle swarm optimization for forecasting flood routing calculations in the Eastern Black Sea Basin, Türkiye.
    Katipoğlu OM; Sarıgöl M
    Environ Sci Pollut Res Int; 2023 Apr; 30(16):46074-46091. PubMed ID: 36715798
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.