Combination of Multiple Data-Driven Models for Long-Term Monthly Runoff Predictions Based on Bayesian Model Averaging
By Hua-ai Huang, Zhongmin Liang, Binquan Li, Dong Wang, Yiming Hu, Yujie Li. Water resources management, 2019
83 citations recorded by the source corpus.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/0e0d1eaf15e911c6efce8f063d4fb48d53976009
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
- Anurag Malik
- Ozgur Kisi
- Quoc Bao Pham
- Rahim Barzegar
- D. Souag-Gamane
- Y. Tikhamarine
- Mohammad Taghi Aalami
In Global and Planetary Change
- Anurag Malik
- Ozgur Kisi
- Jan Adamowski
- D. Souag-Gamane
- Y. Tikhamarine
- Quoc Bao Pham
- Masoud Karbasi
- Hossein Bonakdari
In Water Science and Technology
- Jan Adamowski
- Anurag Malik
- Rahim Barzegar
- D. Souag-Gamane
- Y. Tikhamarine
- Mohammad Taghi Aalami
- Quoc Bao Pham
- Ozgur Kisi
Citations in the corpus
- Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, result): “Hussain and Khan (2020) applied multilayer perceptron (MLP), support vector regression (SVR) and RF models to forecast the monthly flow in the Hunza River, Pakistan, and they found that the RF model outperformed to the others in the study basin.”
- Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, methodology): “Initially, the purpose of the SVR was to convert a low-dimensional nonlinear feature into a high-dimensional linear feature through kernel functions to avoid dimensionality (Huang et al. 2019).”