Daily reservoir inflow forecasting using artificial neural networks with stopped training approach

By Paulin Coulibaly, François Anctil, Bernard Bobée. 2000

609 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/82a58024ba90d38ae348bead0b35c8f92a4ad999

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In Electrical and Electronic Engineering

In Environmental Engineering

In Water Science and Technology

Citations in the corpus

A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer (2011, methodology): “Input selection method using statistical properties such as cross-correlation has been often employed for the research of time-series data forecasting (Sajikumar and Thandaveswara, 1999; Coulibaly et al., 2000; Sudheer et al., 2002; Nayak et al., 2006).”
Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment (2014, background): “For the criterion, all the data were divided into three sets (Coulibaly et al. 2000).”
Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir (2013, result): “12 shows a compression between current study and previous studies (Abrahart et al., 2001; Baareh et al., 2006; Balaguer et al., 2008; Banihabib et al., 2008; Chiang et al., 2007; Coulibaly et al., 2000; Heidarnejad and Gholami, 2012; Karunasinghe and Liong, 2006; Kisi and Cigizoglu, 2005; Pulido-Cal”
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).”
Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. (2010, background): “…a Karstic watershed (Labat et al., 1999), to characterize daily streamflow (Smith et al., 1998; Saco and Kumar, 2000) and monthly reservoir inflow (Coulibaly et al., 2000), to evaluate rainfall–runoff models (Lane, 2007), to analyze streamflow trends (Adamowski et al., 2009), and to forecast…”
Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration (2010, methodology): “To improve the generalization of these models, the stop training algorithm (STA) is used (Coulibaly et al., 2000).”
Groundwater level forecasting using artificial neural networks (2005, neutral): “Nevertheless, many researchers have been successfully using it (Anctil et al., 2004; Coulibaly et al., 2000; Coulibaly et al., 2001a,b,c; Maier and Dandy, 1998; Maier and Dandy, 2000; Toth et al., 2000).”
Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon (2012, methodology): “A similar procedure for selecting the FNN inputs has been reported by Coulibaly et al. (2000), though the authors made use of an Auto-Regressive Moving Average with eXogenous inputs (ARMAX) instead.”
Drought forecasting using artificial neural networks and time series of drought indices (2007, methodology): “Although there is now a significant number of network types and training algorithms, this paper employs the Multi-Layer Perceptron (MLP) – the most widespread (in hydrological research) topological tool at present (Coulibaly et al., 2000; Maier and Dandy, 2000).”
A review on the applications of wavelet transform in hydrology time series analysis (2013, background): “, 2005; Bruni and Vitulano, 2006; Chanerley and Alexander, 2007); (5) wavelet cross-correlation analysis of hydrologic series (Labat, 2005); and (6) wavelet-based hydrologic simulation and forecasting (Coulibaly et al., 2000).”
Multi-models for SPI drought forecasting in the north of Haihe River Basin, China (2017, background): “1998) and monthly reservoir inflow (Coulibaly et al. 2000).”