Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting-Reference-Cited by-同舟云学术

Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting

Published:2010-04 Issue:4 Volume:15 Page:275-283
ISSN:1084-0699
Container-title:Journal of Hydrologic Engineering
language:en
Short-container-title:J. Hydrol. Eng.

Author:

Yonaba H.¹²³,Anctil F.¹²³,Fortin V.¹²³

Affiliation:

1. Ph.D. Candidate, Dept. of Civil Engineering, Université Laval, Pavillon Adrien-Pouliot, 1065, Avenue de la Médecine, Quebec, PQ, Canada G1V 0A6.

2. Professor, Dept. of Civil Engineering, Université Laval, Pavillon Adrien-Pouliot, 1065, Avenue de la Médecine, Quebec, PQ, Canada G1V 0A6 (corresponding author).

3. Researcher, Environment Canada, Canadian Meteorological Centre, 2121, North Service Road, Trans-Canada Highway, Dorval, PQ, Canada H9P 1J3.

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.1943-5584.0000188

Reference51 articles.

1. Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions

2. A soil moisture index as an auxiliary ANN input for stream flow forecasting

3. Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models

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

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