River Flow Modeling Using Artificial Neural Networks

By Ozgur Kisi. 2004

307 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/4ce5a434a94ca8d69a02bd35fb5642d2aac52c6d

How researchers see this paper

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

In Artificial Intelligence

1. Jan Adamowski
2. Kwok-Wing Chau
3. C. Wu
4. Yan Li
5. K. Sun
6. B. Ozga-Zieliński
7. A. Sliusarieva
8. Hiu Fung Chan

In Electrical and Electronic Engineering

1. Jan Adamowski
2. Kwok-Wing Chau
3. C. Wu
4. Hossein Bonakdari
5. Yan Li
6. K. Sun
7. B. Ozga-Zieliński
8. A. Sliusarieva

In Environmental Engineering

1. Jan Adamowski
2. Kwok-Wing Chau
3. Hossein Bonakdari
4. Ozgur Kisi
5. C. Wu
6. Ravinesh C. Deo
7. Yan Li
8. K. Sun

Citations in the corpus

• Neural Granger Causality (2021, background): “Indeed, their time series variants, such as autoregressive multilayer perceptrons (MLPs) [23], [24], [25] and recurrent neural networks (RNNs) like long-short term memory networks (LSTMs) [26] have shown impressive performance in forecasting multivariate time series given their past [27], [28], [29]”
• Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones (2005, background): “…(Sudheer et al. , 2002; Wilby et al ., 2003; Solomatine & Dulal, 2003), streamflow prediction (Raman & Sunilkumar, 1995; Zealand et al. , 1999; Chibanga et al ., 2003; Cigizoglu, 2003; Kisi, 2004a; Cigizoglu & Kisi, 2005) and reservoir inflow forecasting (Saad et al. , 1996; Jain et al. , 1999).”
• Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada (2012, methodology): “…forecasting such as rain-fall-runoff modeling, streamflow forecasting, groundwater and precipitation forecasting, and water quality issues [ Kisi , 2004; Sahoo and Ray , 2006; Adamowski , 2007, 2008a, 2008b; Banerjee et al. , 2009; Pramanik and Panda , 2009; Sreekanth et al. , 2009; Sethi…”
• Methods to improve neural network performance in daily flows prediction (2009, methodology): “15 Kişi, O., (2005). Daily river flow forecasting using artificial neural networks and auto-regressive models, 557 Turkish J. Eng. Env. Sci, 29, 9-20. 558 Kişi, O., (2008). Constructing neural network sediment estimation models using a data-driven algorithm, 559 Mathematics and Computers in Simulati”
• Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. (2010, methodology): “Kisi (2004) used MLP ANN models for monthly flow forecasts.”
• Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model (2017, background): “A number of researchers have employed such models for making hydrological predictions (Jain et al. 1999; Coulibaly et al. 2000; Xu and Li 2002; Anctil et al. 2004; Kisi 2004; Kisi and Cigizoglu 2005, 2007; Firat and Güngör 2010; Valipour et al. 2012b; Valipour 2016).”