A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

By Zhongmin Liang, Yujie Li, Yiming Hu, Binquan Li, Jun Wang. Theoretical and Applied Climatology, 2018

86 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/ffc5c37668e28ba81c4ce6753d7d61e93b405153

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

1. Amir Mosavi
2. Pınar Öztürk
3. Kwok-Wing Chau
4. Haitham Abdulmohsin Afan
5. Ozgur Kisi
6. Hossein Bonakdari
7. Isa Ebtehaj
8. U. Aksoy

In Global and Planetary Change

1. Amir Mosavi
2. Pınar Öztürk
3. Kwok-Wing Chau
4. Ozgur Kisi
5. U. Aksoy
6. B. B. Ekici

In Water Science and Technology

1. Amir Mosavi
2. Pınar Öztürk
3. Kwok-Wing Chau
4. Ozgur Kisi
5. U. Aksoy
6. B. B. Ekici

Citations in the corpus

• Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors (2019, methodology): “The SVM and a hydrologic uncertainty processor (HUP) were used for the monthly runoff predication with consideration to the SST index [24].”
• Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “A revision and discussion of these methods follow, identifying the most suitable methods presented in the literature.”