BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

601 related articles for article (PubMed ID: 32445152)

  • 1. 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]  

  • 2. Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm.
    Tikhamarine Y; Malik A; Pandey K; Sammen SS; Souag-Gamane D; Heddam S; Kisi O
    Environ Monit Assess; 2020 Oct; 192(11):696. PubMed ID: 33040211
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET
    Heddam S; Watts MJ; Houichi L; Djemili L; Sebbar A
    Environ Monit Assess; 2018 Aug; 190(9):516. PubMed ID: 30109518
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Reference evapotranspiration estimate with missing climatic data and multiple linear regression models.
    Koç DL; Erkan Can M
    PeerJ; 2023; 11():e15252. PubMed ID: 37131990
    [TBL] [Abstract][Full Text] [Related]  

  • 5. SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation.
    Keshtegar B; Heddam S; Sebbar A; Zhu SP; Trung NT
    Environ Sci Pollut Res Int; 2019 Dec; 26(35):35807-35826. PubMed ID: 31705408
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Assessment of pan coefficient models for the estimation of the reference evapotranspiration in a Mediterranean environment in Turkey.
    Koç DL
    PeerJ; 2022; 10():e13554. PubMed ID: 35698619
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions.
    Laaboudi A; Mouhouche B; Draoui B
    Int J Biometeorol; 2012 Sep; 56(5):831-41. PubMed ID: 21910034
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Assessing the physical and empirical reference evapotranspiration (ETo) models and time series analyses of the influencing weather variables on ETo in a semi-arid area.
    Ahmadi SH; Javanbakht Z
    J Environ Manage; 2020 Dec; 276():111278. PubMed ID: 32906072
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Performance evaluation of different empirical models for reference evapotranspiration estimation over Udhagamandalm, The Nilgiris, India.
    Raja P; Sona F; Surendran U; Srinivas CV; Kannan K; Madhu M; Mahesh P; Annepu SK; Ahmed M; Chandrasekar K; Suguna AR; Kumar V; Jagadesh M
    Sci Rep; 2024 May; 14(1):12429. PubMed ID: 38816436
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms.
    Malik A; Tikhamarine Y; Sammen SS; Abba SI; Shahid S
    Environ Sci Pollut Res Int; 2021 Aug; 28(29):39139-39158. PubMed ID: 33751346
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study.
    Elzain HE; Abdalla OA; Abdallah M; Al-Maktoumi A; Eltayeb M; Abba SI
    J Environ Manage; 2024 Mar; 354():120246. PubMed ID: 38359624
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region.
    Ferreira LB; Cunha FFD; Silva GHD; Campos FB; Dias SHB; Santos JEO
    An Acad Bras Cienc; 2021; 93(1):e20200304. PubMed ID: 33787689
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Assessing of evapotranspiration models using limited climatic data in Southeast Anatolian Project Region of Turkey.
    Aydın Y
    PeerJ; 2021; 9():e11571. PubMed ID: 34178458
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Evaluation of Empirical and Machine Learning Approaches for Estimating Monthly Reference Evapotranspiration with Limited Meteorological Data in the Jialing River Basin, China.
    Luo J; Dou X; Ma M
    Int J Environ Res Public Health; 2022 Oct; 19(20):. PubMed ID: 36293705
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Estimating the monthly pan evaporation with limited climatic data in dryland based on the extended long short-term memory model enhanced with meta-heuristic algorithms.
    Fu T; Li X
    Sci Rep; 2023 Apr; 13(1):5960. PubMed ID: 37045898
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models.
    Alam MM; Akter MY; Islam ARMT; Mallick J; Kabir Z; Chu R; Arabameri A; Pal SC; Masud MAA; Costache R; Senapathi V
    J Environ Manage; 2024 Feb; 351():119714. PubMed ID: 38056328
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing.
    Dias SHB; Filgueiras R; Fernandes Filho EI; Arcanjo GS; Silva GHD; Mantovani EC; Cunha FFD
    PLoS One; 2021; 16(2):e0245834. PubMed ID: 33561147
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Estimation of daily reference evapotranspiration by hybrid singular spectrum analysis-based stochastic gradient boosting.
    Başakın EE; Ekmekcioğlu Ö; Stoy PC; Özger M
    MethodsX; 2023; 10():102163. PubMed ID: 37077895
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Performance assessment of artificial neural networks and support vector regression models for stream flow predictions.
    Ateeq-Ur-Rauf ; Ghumman AR; Ahmad S; Hashmi HN
    Environ Monit Assess; 2018 Nov; 190(12):704. PubMed ID: 30406854
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China.
    Zhao L; Zhao X; Li Y; Shi Y; Zhou H; Li X; Wang X; Xing X
    Environ Sci Pollut Res Int; 2023 Feb; 30(9):22396-22412. PubMed ID: 36289123
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 31.