Cognitive computing models for estimation of reference evapotranspiration: A review-Reference-Cited by-同舟云学术

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Cognitive computing models for estimation of reference evapotranspiration: A review

Published:2021-12 Issue: Volume:70 Page:109-116
ISSN:1389-0417
Container-title:Cognitive Systems Research
language:en
Short-container-title:Cognitive Systems Research

Author:

Hebbalaguppae Krishnashetty Pradeep,Balasangameshwara Jasma,Sreeman Sheshshayee,Desai Sujeet,Bengaluru Kantharaju Archana

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Experimental and Cognitive Psychology,Software

Reference64 articles.

1. Gene-expression programming for sediment transport in sewer pipe systems;Ab. Ghani;Journal of Pipeline Systems Engineering and Practice,2011

2. Performance evaluation of ANN and ANFIS models for estimating garlic crop evapotranspiration;Abyaneh;Journal of Irrigation and Drainage Engineering,2011

3. Evapotranspiration modeling using second-order neural networks;Adamala;Journal of Hydrologic Engineering,2014

4. Future long-term changes in global water resources driven by socio-economic and climatic changes;Alcamo;Hydrological Sciences Journal,2007

5. Modelling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies;Alizamir;Acta Geophysica,2020

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Use of gene expression programming to predict reference evapotranspiration in different climatic conditions;Applied Water Science;2024-06-08

2. Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration;Water Resources Management;2024-02-07

3. Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast;Advances in Meteorology;2024-02-03

4. Monthly potential evapotranspiration estimated using the Thornthwaite method with gridded climate datasets in Southeastern Brazil;Theoretical and Applied Climatology;2024-01-29

5. Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow;Computer Modeling in Engineering & Sciences;2024

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