Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models

By Yun Bai, Zhiqiang Chen, Jingjing Xie, Chuan Li. 2016

181 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/9a4ef8b0f1f0c2d0c27ac1f5f72e04569dd07310

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Kwok-Wing Chau
2. Manuel Herrera
3. Henry Leung
4. Shahaboddin Shamshirband
5. Farnaz Fotovatikhah
6. Md. Jalil Piran
7. Chaopeng Shen
8. Sina Ardabili

In Environmental Engineering

1. Chaopeng Shen
2. Kwok-Wing Chau
3. Shahaboddin Shamshirband
4. Manuel Herrera
5. Henry Leung
6. Farnaz Fotovatikhah
7. Md. Jalil Piran
8. Sina Ardabili

In Ocean Engineering

1. Kwok-Wing Chau
2. Henry Leung
3. Chaopeng Shen

Citations in the corpus

• A Review of Deep Learning Models for Time Series Prediction (2019, background): “2) Representative Deep Learning Model - Hybrids: Besides the above-mentioned studies reporting individual deep learning models, there also exist some hybrids or combinational structures which are successfully applied on time series prediction [205], [206].”
• A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “Bai et al. (2016) applied nonrecurrent DBN to predict SHEN 8572 single-site time series of inflow to the Three Gorges reservoir.”
• Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (2018, methodology): “Bai, Chen, Xie, and Li (2016) considered multi-scale deep feature learning equipped with hybrid models for predicting the daily reservoir inflows using the historical daily data of the Three Gorges reservoir in China.”