Artificial neural network modeling of water table depth fluctuations
By Paulin Coulibaly, François Anctil, R. Aravena, Bernard Bobée. 2001
395 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Environmental Engineering, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/997844b88e5234a33e8d62f729fed90b65538936
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
In Environmental Engineering
- Kwok-Wing Chau
- F. Mekanik
- Monzur Alam Imteaz
- Riccardo Taormina
- S. Gato-Trinidad
- Rajandrea Sethi
- Amgad Elmahdi
- Ravinesh C. Deo
In Water Science and Technology
- Kwok-Wing Chau
- F. Mekanik
- Monzur Alam Imteaz
- Riccardo Taormina
- Rajandrea Sethi
- Ravinesh C. Deo
- Ozgur Kisi
- Amir Mosavi
Citations in the corpus
- Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes (2013, background): “, 2004; Turan and Yurdusev, 2009) and for ground water modelling (Coulibaly et al., 2001; Daliakopoulos et al., 2005; Rogers and Dowla, 1994).”
- A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer (2011, neutral): “Nayak et al. (2006) and Krishna et al.”
- Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon (2012, methodology): “Coulibaly et al. (2001) used different types of ANNs for monthly predictions of groundwater levels in the Gondo Plain, Burkina Faso.”