Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management-Reference-Cited by-同舟云学术

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Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management

Published:2024-02 Issue: Volume:912 Page:169403
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Hailegnaw Niguss Solomon,Bayabil Haimanote K.,Berihun Mulatu Liyew,Teshome Fitsum Tilahun,Shelia Vakhtang,Getachew Fikadu

Funder

U.S. Department of Agriculture

National Institute of Food and Agriculture

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference71 articles.

1. Evapotranspiration modeling using different tree based Ensembled machine learning algorithm;Agrawal;Water Resour. Manag.,2022

2. Towards smart irrigation: a literature review on the use of geospatial technologies and machine learning in the management of water resources in arboriculture;Ahansal;Agronomy,2022

3. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56;Allen,1998

4. Evapotranspiration information reporting: I. Factors governing measurement accuracy;Allen;Agric. Water Manag.,2011

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

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