Hybrid Wavelet-M5 Model Tree for Rainfall-Runoff Modeling-Reference-Cited by-同舟云学术

Hybrid Wavelet-M5 Model Tree for Rainfall-Runoff Modeling

Published:2019-05 Issue:5 Volume:24 Page:04019012
ISSN:1084-0699
Container-title:Journal of Hydrologic Engineering
language:en
Short-container-title:J. Hydrol. Eng.

Author:

Nourani Vahid¹,Davanlou Tajbakhsh Ali²,Molajou Amir³,Gokcekus Huseyin⁴

Affiliation:

1. Professor, Dept. of Water Resources Engineering, Faculty of Civil Engineering, Univ. of Tabriz, 51666 Tabriz, Iran; Professor, Dept. of Civil Engineering, Near East Univ., P.O. Box 99138, Nicosia, North Cyprus, Mersin 10, Turkey (corresponding author).

2. Dept. of Water Resources Engineering, Faculty of Civil Engineering, Univ. of Tabriz, 51666 Tabriz, Iran.

3. Ph.D. Student, Dept. of Water Resources Engineering, Faculty of Civil Engineering, Iran Univ. of Science and Technology, 51546 Tehran, Iran.

4. Professor, Dept. of Civil Engineering, Near East Univ., Turkish Republic of Northern Cyprus, Cyprus 99138, Turkey.

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.1943-5584.0001777

Reference25 articles.

1. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

2. Comparison of ANNs and Empirical Approaches for Predicting Watershed Runoff

3. Closure to “Comparison of ANNs and Empirical Approaches for Predicting Watershed Runoff” by Jagadeesh Anmala, Bin Zhang, and Rao S. Govindaraju

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

5. Neural networks and M5 model trees in modelling water level–discharge relationship

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