Deriving Spatially Distributed Precipitation Data Using the Artificial Neural Network and Multilinear Regression Models-Reference-Cited by-同舟云学术

Deriving Spatially Distributed Precipitation Data Using the Artificial Neural Network and Multilinear Regression Models

Published:2013-02 Issue:2 Volume:18 Page:194-205
ISSN:1084-0699
Container-title:Journal of Hydrologic Engineering
language:en
Short-container-title:J. Hydrol. Eng.

Author:

Sharma Suresh¹,Isik Sabahattin²,Srivastava Puneet³,Kalin Latif⁴

Affiliation:

1. Ph.D. Student, Biosystems Engineering Dept., Tom E. Corley Building, Auburn Univ., Auburn, AL 36849.

2. Postdoctorate, School of Forestry and Wildlife Sciences, Forestry and Wildlife Building, Auburn Univ., Auburn, AL 36849.

3. Associate Professor, Biosystems Engineering Dept., Tom E. Corley Building, Auburn Univ., Auburn, AL 36849 (corresponding author).

4. Associate Professor, School of Forestry and Wildlife Sciences, Forestry and Wildlife Building, Auburn Univ., Auburn, AL 36849.

Publisher

American Society of Civil Engineers (ASCE)

Subject

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

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29HE.1943-5584.0000617

Reference70 articles.

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3. Artificial Neural Networks in Hydrology. II: Hydrologic Applications

4. A COMPARISON OF SPATIAL INTERPOLATION METHODS FOR ESTIMATION OF AVERAGE ELECTROMAGNETIC FIELD MAGNITUDE

5. Rainfall Estimation by the WSR-88D for Heavy Rainfall Events

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