Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data-Reference-Cited by-同舟云学术

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Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data

Published:2019-10 Issue: Volume:165 Page:104937
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Reis Matheus Mendes,da Silva Ariovaldo José,Zullo Junior Jurandir,Tuffi Santos Leonardo David,Azevedo Alcinei Místico,Lopes Érika Manuela Gonçalves

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference38 articles.

1. Crop evapotranspiration - guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56;Allen;Fao, Rome,1998

2. Global performance ranking of temperature-based approaches for evapotranspiration estimation considering Köppen climate classes;Almorox;J. Hydrol.,2015

3. Simulation of crop evapotranspiration and crop coefficients with data in weighing lysimeters;Anapalli;Agric. Water Manag.,2016

4. Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables;Antonopoulos;Comput. Electron. Agric.,2017

5. Extreme learning machine and adaptive sparse representation for image classification;Cao;Neural Networks,2016

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