Artificial Neural Network–Based Drought Forecasting Using a Nonlinear Aggregated Drought Index-Reference-Cited by-同舟云学术

Artificial Neural Network–Based Drought Forecasting Using a Nonlinear Aggregated Drought Index

Published:2012-12 Issue:12 Volume:17 Page:1408-1413
ISSN:1084-0699
Container-title:Journal of Hydrologic Engineering
language:en
Short-container-title:J. Hydrol. Eng.

Author:

Barua S.¹,Ng A. W. M.²,Perera B. J. C.³

Affiliation:

1. School of Engineering and Science, Victoria Univ., Melbourne, Victoria 8001, Australia (corresponding author).

2. Senior Lecturer, School of Engineering and Science, Victoria Univ., Melbourne, Victoria 8001, Australia.

3. Professor, Faculty of Health, Engineering and Science, Victoria Univ., Melbourne, Victoria 8001, Australia.

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.1943-5584.0000574

Reference29 articles.

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

2. Barua S. Ng A. W. M. and Perera B. J. C. (2009). “Development of an aggregated drought index using hydrometeorological variables.” Proc. Australia’s National Water Conf. and Exhibition: Ozwater’09 Australian Water Association Artarmon NSW Australia.

3. Comparative Evaluation of Drought Indexes: Case Study on the Yarra River Catchment in Australia

4. Drought forecasting using an aggregated drought index and artificial neural network

5. Objective Quantification of Drought Severity and Duration

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