Forecasting of Reference Evapotranspiration by Artificial Neural Networks-Reference-Cited by-同舟云学术

Forecasting of Reference Evapotranspiration by Artificial Neural Networks

Published:2003-12 Issue:6 Volume:129 Page:454-457
ISSN:0733-9437
Container-title:Journal of Irrigation and Drainage Engineering
language:en
Short-container-title:J. Irrig. Drain Eng.

Author:

Trajkovic Slavisa¹,Todorovic Branimir²,Stankovic Miomir³

Affiliation:

1. Assistant Professor, Faculty of Civil Engineering, Univ. of Nis, Beogradska 14, 18000 Nis, Serbia and Montenegro.

2. Resident Assistant, Faculty of Occupational Safety, Univ. of Nis, Carnojevica 10a, 18000 Nis, Serbia and Montenegro.

3. Professor, Faculty of Occupational Safety, Univ. of Nis, Carnojevica 10a, 18000 Nis, Serbia and Montenegro.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Agricultural and Biological Sciences (miscellaneous),Water Science and Technology,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%290733-9437%282003%29129%3A6%28454%29

Reference22 articles.

1. Allen R. G. Pereira L. S. Raes D. and Smith M. (1998). “Crop evapotranspiration: Guidelines for computing crop water requirements.” FAO Irrigation and Drainage Paper 56 Food and Agricultural Organization of the United Nations (FAO) Rome.

2. Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers

3. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach

4. Performance Evaluation of Artificial Neural Networks for Runoff Prediction

5. Runoff Forecasting Using RBF Networks with OLS Algorithm

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