Groundwater level forecasting using artificial neural networks

By Ioannis N. Daliakopoulos, Paulin Coulibaly, Ioannis K. Tsanis. 2005

673 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/9d7ed0b159828069933102a575554abff16fe2fe

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. Kwok-Wing Chau
2. Mohammad Zounemat‐Kermani
3. F. Mekanik
4. K. Hasan
5. Rajandrea Sethi
6. Monzur Alam Imteaz
7. M. J. Alam
8. Riccardo Taormina

In Electrical and Electronic Engineering

1. Kwok-Wing Chau
2. Mohammad Zounemat‐Kermani
3. Meysam Alizamir
4. F. Mekanik
5. S. Gato-Trinidad
6. K. Hasan
7. Rajandrea Sethi
8. Monzur Alam Imteaz

In Environmental Engineering

1. Kwok-Wing Chau
2. Mohammad Zounemat‐Kermani
3. Ozgur Kisi
4. Meysam Alizamir
5. F. Mekanik
6. Monzur Alam Imteaz
7. Riccardo Taormina
8. S. Gato-Trinidad

Citations in the corpus

• Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods (2012, background): “Radial basis function networks tend to learn much faster than a MLP (Mason et al., 1996; Daliakopoulos et al., 2005).”
• Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon (2012, methodology): “Daliakopoulos et al. (2005) tested the performance of several types of ANNs and training algorithms to forecast monthly groundwater fluctuations in an aquifer in the Messara Valley, Crete, up to 18 months ahead.”
• 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).”
• Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods (2014, background): “Since then, ANNs have enjoyed their popularity in many applications including hydrological processes (Abrahart et al. 2004; Altunkaynak 2007; Altunkaynak and Strom 2009; Alvisi et al. 2006; ASCE Committee 2000; Bardossy et al. 2006; Campolo et al. 1999; Dawson and Wilby 2001; Daliakopoulos et al. 20”
• Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh (2017, background): “…phenomena, such as temperature predictions (S ahin, 2012; Chenard and Caissie, 2008), rainfall predictions (French et al., 1992; Aksoy and Dahamsheh, 2009; Mandal and Jothiprakash, 2012; Farajzadeh et al., 2014), and groundwater level forecasts (Daliakopoulos et al., 2005;Yang et al., 2009).”
• Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data (2017, methodology): “It is worth noting that in some zones, groundwater is an authentic supply source, whereas in other areas it might be chosen as a water supply due to its accessibility (Daliakopoulos et al. 2005; Nayak et al. 2006).”