Forecasting groundwater level using artificial neural networks.

By P. Sreekanth, N. Geethanjali, P. Sreedevi, Shakeel Ahmed, N. R. Kumar, P. Jayanthi. 2009

126 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b1c263108322a5fc3f305976851ed2bb8ff6d262

How researchers see this paper

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

1. Jan Adamowski
2. B. Ozga-Zieliński
3. Hiu Fung Chan
4. Shiv O. Prasher
5. A. Sliusarieva
6. Ravinesh C. Deo
7. Yurui Fan
8. Mukesh Tiwari

Citations in the corpus

• 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): “…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 et al. , 2010; Adamow-ski and Chan , 2011].”