Evapotranspiration Modeling Using Second-Order Neural Networks

Author:

Adamala Sirisha1,Raghuwanshi N. S.2,Mishra Ashok3,Tiwari Mukesh K.4

Affiliation:

1. Research Scholar, Agricultural and Food Engineering Dept., Indian Institute of Technology, Kharagpur, West Bengal 721302, India.

2. Professor, Agricultural and Food Engineering Dept., Indian Institute of Technology, Kharagpur, West Bengal 721302, India.

3. Associate Professor, Agricultural and Food Engineering Dept., Indian Institute of Technology, Kharagpur, West Bengal 721302, India (corresponding author).

4. Assistant Professor, Soil and Water Engineering Dept., College of Agricultural Engineering and Technology, Anand Agricultural Univ., Godhra, Gujarat 389001, India.

Publisher

American Society of Civil Engineers (ASCE)

Subject

General Environmental Science,Water Science and Technology,Civil and Structural Engineering,Environmental Chemistry

Reference41 articles.

1. Comparison of artificial neural network and physically based models for estimating of reference evapotranspiration in greenhouse;Abedi-Koupai J.;Aust. J. Basic Appl. Sci.,2009

2. Infilling Missing Daily Evapotranspiration Data Using Neural Networks

3. Performance Evaluation of ANN and ANFIS Models for Estimating Garlic Crop Evapotranspiration

4. Allen R. G. Pereira L. S. Raes D. and Smith M. (1998). “Crop evapotranspiration: Guidelines for computing crop water requirements.” Irrigation and drainage paper no. 56. FAO Rome.

5. Artificial Neural Networks in Hydrology. I: Preliminary Concepts

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