Publications concerning the IVS4EM tools and their applications to different environmental modelling contexts.

Partial Mutual Information

1. Sharma, A., 2000. Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 – a strategy for system predictor identification. Journal of Hydrology 239, 232–239.
2. Bowden, G.J., Maier, H.R., Dandy, G.C., 2005. Input determination for neural network models in water resources applications. Part 1. Background and methodology. Journal of Hydrology 301, 75–92.
3. Bowden, G.J., Maier, H.R., Dandy, G.C., 2005. Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river. Journal of Hydrology 301, 93–107.
4. Kingston, G., Maier, H., Lambert, M., 2006. Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks, in: IEEE World Congress of Computational Intelligence, AAAI Press. pp. 129-134.
5. May, R.J., Maier, H.R., Dandy, G.C., Fernando, T.M.K.G., 2008. Nonlinear variable selection for artificial neural networks using partial mutual information. Environmental Modelling & Software 23, 1312-1326.
6. May, R., Dandy, G., Maier, H., Nixon, J., 2008. Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environmental Modelling & Software 23, 1289–1299.
7. Fernando, T.M.K.G., Maier, H.R., Dandy, G.C., 2009. Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach. Journal of Hydrology 367, 165-176.
8. He, J., Valeo, C., Chu, A., Neumann, N.F., 2011. Prediction of event-based stormwater runoff quantity and quality by ANNs developed using PMI based input selection. Journal of Hydrology 400, 10-23.
9. Wu, W., May, R., Maier, H., Dandy, G., 2013. A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks. Water Resources Research 49, 7598-7614.
10. Sharma, A., Mehrotra, R., 2014. An information theoretic alternative to model a natural system using observational information alone. Water Resources Research 50, 650-660.

Iterative Input variable Selection

1. Castelletti, A.,  S. Galelli, M. Restelli, and R. Soncini-Sessa (2011),  Tree-based features selection for dimensionality reduction of large-scale control system, in Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, 11-15 April, Paris, F. (IIS for model reduction)
2. Castelletti, A., Galelli, S., Restelli, S., Soncini-Sessa, R., 2012. Data-driven dynamic emulation modelling for the optimal management of environmental systems. Environmental Modelling & Software 34, 30–43. (IIS for model reduction)
3. Galelli, S., Castelletti, A., 2013. Tree-based iterative input variable selection for hydrological modelling. Water Resources Research 49, 4295–4310. (the IIS algorithm)
4. Galelli, S., Castelletti, A., 2013. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling. Hydrology and Earth System Sciences 17, 2669–2684. (extra trees used in IIS)
5. Fornarelli, R., S. Galelli, A. Castelletti,  J.P. Antenucci, and C. Marti (2013), An empirical modeling approach to predict phytoplancton dynamics in a reservoir affcted by inter-basin water transfers. Water Resources Research, 49(6), 3626–3641, doi: 10.1002/wrcr.20268 (IIS of water quality modelling)
   
6. Surridge,  B.W.J.,  S. Bizzi, A. Castelletti, A framework for coupling explanation and prediction in hydroecological modelling (2014), Environmental Modelling & Software,  http://dx.doi.org/10.1016/j.envsoft.2014.02.012. (IIS for spatially distributed hydroecological data)

Partial Correlation Input Selection

1. May, R.J., Maier, H.R., Dandy, G.C., Fernando, T.M.K.G., 2008. Non- linear variable selection for artificial neural networks using partial mutual information. Environmental Modelling & Software 23, 1312–1326.

GA-ANN

1. Bowden, G.J., Maier, H.R., Dandy, G.C., 2005. Input determination for neural network models in water resources applications. Part 1. Background and methodology. Journal of Hydrology 301, 75–92.