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Most water reservoir operators make use of forecasts to inform their decisions and enhance water systems flexibility and resilience by anticipating hydrological extremes. Yet, despite numerous candidate hydro-meteorological variables and forecast horizons may potentially be beneficial to operations, the best information set for a given problem is often not evident. Additionally, in multi-purpose systems characterized by multiple demands with varying vulnerabilities and temporal scales, this information set might change according to the objective tradeoff. In this work, we contribute a novel method to learn the optimal policy representation (i.e., policy input set) by combining a feature selection routine with a multi-objective Direct Policy Search framework in order to retrieve the best policy input set online (i.e., while learning the policy) and dynamically with the objective trade-off. The selected policy search routine is the Neuro-Evolutionary Multi-Objective Direct Policy Search (NEMODPS) which generates flexible policy shapes adaptive to online changes in the input set. This approach is demonstrated on the case study of Lake Como (Italy), where the operating objectives are highly heterogeneous in their dynamics (fast and slow) and vulnerabilities (wet and dry extremes). We show how varying objectives, and tradeoffs therein, benefit from a different policy representation, ultimately yielding remarkable results in terms of conflict mitigation between different users. More informed policies, moreover, show higher robustness when re-evaluated across a suite of different hydrological conditions.

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