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

**Partial Mutual Information**

- 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. - 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. - 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. - Kingston, G., Maier, H., Lambert, M., 2006. Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks, in: IEEE World Congress of Computational Intelligence, AAAI Press. pp. 129-134.
- May, R.J., Maier, H.R., Dandy, G.C., Fernando, T.M.K.G., 2008. Nonlinear variable selection for artificial neural networks using partial mutual information.
*Environmental Modelling & Software*23, 1312-1326. - 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. E
*nvironmental Modelling & Software*23, 1289–1299. - 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. - 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. - Wu, W., May, R., Maier, H., Dandy, G., 2013. A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks.
*Water Resources Research*49, 7598-7614. - 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**

- 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)
- Castelletti, A., Galelli, S., Restelli, S., Soncini-Sessa, R., 2012. Data-driven dynamic emulation modelling for the optimal management of environmental systems. E
*nvironmental Modelling & Software*34, 30–43. (IIS for model reduction) - Galelli, S., Castelletti, A., 2013. Tree-based iterative input variable selection for hydrological modelling.
*Water Resources Research*49, 4295–4310. (the IIS algorithm) - 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) - 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)

- 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**

- 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**

- 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.