Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model

By Hamid Moeeni, Hossein Bonakdari. Stochastic environmental research and risk assessment (Print), 2017

76 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/84b291e2f8e774d1c0e5d9dbcfc0559d79aa0803

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. Xiangang Luo
2. Shuang Zhu
3. Lingsheng Meng
4. Xiaohui Yuan
5. Jing Peng
6. Zhanya Xu

In Environmental Engineering

1. Xiangang Luo
2. Shuang Zhu
3. Lingsheng Meng
4. Xiaohui Yuan
5. Jing Peng
6. Zhanya Xu

Citations in the corpus

• A hybrid support vector regression framework for streamflow forecast (2019, methodology): “Artificial Neural Network (ANN) is a widely used method for long-term simulation and forecast (Aksoy and Dahamsheh, 2009; Moeeni and Bonakdari, 2016; Wu and Chau, 2010; Wu et al., 2009).”