Articles | Volume 22, issue 5
Hydrol. Earth Syst. Sci., 22, 2987–3006, 2018
Hydrol. Earth Syst. Sci., 22, 2987–3006, 2018

Research article 22 May 2018

Research article | 22 May 2018

Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems

Jason M. Hunter et al.

Related authors

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application
Matthew S. Gibbs, David McInerney, Greer Humphrey, Mark A. Thyer, Holger R. Maier, Graeme C. Dandy, and Dmitri Kavetski
Hydrol. Earth Syst. Sci., 22, 871–887,,, 2018
Short summary
Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones
Danlu Guo, Seth Westra, and Holger R. Maier
Hydrol. Earth Syst. Sci., 21, 2107–2126,,, 2017
Short summary

Related subject area

Subject: Water Resources Management | Techniques and Approaches: Modelling approaches
Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK
Andres Peñuela, Christopher Hutton, and Francesca Pianosi
Hydrol. Earth Syst. Sci., 24, 6059–6073,,, 2020
Short summary
From skill to value: isolating the influence of end user behavior on seasonal forecast assessment
Matteo Giuliani, Louise Crochemore, Ilias Pechlivanidis, and Andrea Castelletti
Hydrol. Earth Syst. Sci., 24, 5891–5902,,, 2020
Short summary
The value of citizen science for flood risk reduction: cost–benefit analysis of a citizen observatory in the Brenta-Bacchiglione catchment
Michele Ferri, Uta Wehn, Linda See, Martina Monego, and Steffen Fritz
Hydrol. Earth Syst. Sci., 24, 5781–5798,,, 2020
Short summary
Risk assessment in water resources planning under climate change at the Júcar River basin
Sara Suárez-Almiñana, Abel Solera, Jaime Madrigal, Joaquín Andreu, and Javier Paredes-Arquiola
Hydrol. Earth Syst. Sci., 24, 5297–5315,,, 2020
Short summary
Interplay of changing irrigation technologies and water reuse: example from the upper Snake River basin, Idaho, USA
Shan Zuidema, Danielle Grogan, Alexander Prusevich, Richard Lammers, Sarah Gilmore, and Paula Williams
Hydrol. Earth Syst. Sci., 24, 5231–5249,,, 2020
Short summary

Cited articles

Adamowski, J. and Chan, H. F.: A wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol, 407, 28–40,, 2011.
Alvarez-Garreton, C., Ryu, D., Western, A. W., Su, C.-H., Crow, W. T., Robertson, D. E., and Leahy, C.: Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes, Hydrol. Earth Syst. Sci., 19, 1659–1676,, 2015.
Banerjee, P., Singh, V. S., Chatttopadhyay, K., Chandra, P. C., and Singh, B.: Artificial neural network model as a potential alternative for groundwater salinity forecasting, J. Hydrol., 398, 212–220,, 2011.
Barnett, S.: Gurra Gurra Wetland Complex – Groundwater Data Review, Dept. of Water, Land and Biodiversity Conservation, 4, 2007.
Beecham, R., Arranz, P., Boddy, J., Burrell, M., Gilmore, R., Javam, A., Martin, J., O'Neill, R., and Salbe, I.: Implementing daily salinity models in the NSW Murray Darling Basin tributaries, in: Modsim 2003, International Congress on Modelling and Simulation, Vol 1–4: Vol 1: Natural Systems, Pt 1; Vol 2: Natural Systems, Pt 2; Vol 3: Socio-Economic Systems; Vol 4: General Systems, 362–367, 2003.
Short summary
This research proposes a generalised hybrid model development framework and applies it to a case study of salinity prediction in a reach of the Murray River. The hybrid model combines five sub-models which describe one process of salt entry each and are developed based on the amount of system knowledge and data that are available to support each individual process. The model demonstrates increased performance over two benchmark models and has implications for future model development processes.