Estimation of daily reference evapotranspiration by neuro computing techniques using limited data in a semi-arid environment-Reference-Cited by-同舟云学术

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Estimation of daily reference evapotranspiration by neuro computing techniques using limited data in a semi-arid environment

Published:2017-12-13 Issue:7 Volume:64 Page:916-929
ISSN:0365-0340
Container-title:Archives of Agronomy and Soil Science
language:en
Short-container-title:Archives of Agronomy and Soil Science

Author:

Banda Paul¹,Cemek Bilal²³,Küçüktopcu Erdem²

Affiliation:

1. RMIT University, School of Civil, Environmental and Chemical Engineering, Melbourne, Australia

2. Ondokuz Mayıs University, Faculty of Agriculture, Department of Agricultural Structures and Irrigation, Samsun, Turkey

3. Agrobigen Ltd Co., Samsun Technopark, Ondokuz Mayis University, Samsun, Turkey

Publisher

Informa UK Limited

Subject

Soil Science,Agronomy and Crop Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/03650340.2017.1414196

Reference43 articles.

1. Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study

2. Comparison of Methods for Estimating REF-ET

3. Estimation, forecasting and extrapolation of river flows by artificial neural networks

4. Data Mining Methods for Knowledge Discovery

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

2. Accurate leaf area index estimation in sorghum using high-resolution UAV data and machine learning models;Physics and Chemistry of the Earth, Parts A/B/C;2024-02

3. Comparison of machine learning techniques and spatial distribution of daily reference evapotranspiration in Türkiye;Applied Water Science;2023-04

4. Hybrid Statistical and Machine Learning Methods for Daily Evapotranspiration Modeling;Sustainability;2023-03-24

5. Development and comparison of artificial intelligence models for estimating daily reference evapotranspiration from limited input variables;Smart Agricultural Technology;2023-02

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