Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting
By Ozgur Kisi. 2011
88 citations recorded by the source corpus.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/481de9aee0b06835929faec62ce8e2114cdc78ef
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
- Jan Adamowski
- A. H. Baghanam
- Vahid Nourani
- Ozgur Kisi
- Xiangang Luo
- Shuang Zhu
- Lingsheng Meng
- Xiaohui Yuan
In Global and Planetary Change
- A. H. Baghanam
- Vahid Nourani
- Jan Adamowski
- Ozgur Kisi
- Xiangang Luo
- Shuang Zhu
- Lingsheng Meng
- Xiaohui Yuan
In Water Science and Technology
- Jan Adamowski
- Vahid Nourani
- A. H. Baghanam
- Ozgur Kisi
- Xiangang Luo
- Shuang Zhu
- Lingsheng Meng
- Xiaohui Yuan
Citations in the corpus
- Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “Kisi (2011b) compared the performance of a Wavelet Regression (WR) technique with ANN models for daily river-stage forecasting.”
- A hybrid support vector regression framework for streamflow forecast (2019, methodology): “Time series decomposition has the ability to analyze streamflow temporal and spatial variation and extracting useful information as much as possible (Kisi, 2011).”