Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
ACM Computing Surveys (CSUR)
Automatic Learning Techniques in Power Systems
Automatic Learning Techniques in Power Systems
Statistical Learning for Humanoid Robots
Autonomous Robots
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
An introduction to variable and feature selection
The Journal of Machine Learning Research
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Machine Learning
Bayesian Networks and participatory modelling in water resource management
Environmental Modelling & Software
Water reservoir control under economic, social and environmental constraints
Automatica (Journal of IFAC)
Environmental Modelling & Software
Non-linear variable selection for artificial neural networks using partial mutual information
Environmental Modelling & Software
Is my model too complex? Evaluating model formulation using model reduction
Environmental Modelling & Software
Position Paper: Modelling with stakeholders
Environmental Modelling & Software
Constructing model credibility in the context of policy appraisal
Environmental Modelling & Software
A methodology for the design and development of integrated models for policy support
Environmental Modelling & Software
Projection-based approaches for model reduction of weakly nonlinear, time-varying systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Environmental Modelling & Software
Surrogate models to compute optimal air quality planning policies at a regional scale
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Model reduction in model predictive control of combined water quantity and quality in open channels
Environmental Modelling & Software
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The optimal management of large environmental systems is often limited by the high computational burden associated to the process-based models commonly adopted to describe such systems. In this paper we propose a novel data-driven Dynamic Emulation Modelling approach for the construction of small, computationally efficient models that accurately emulate the main dynamics of the original process-based model, but with less computational requirements. The approach combines the many advantages of data-based modelling in representing complex, non-linear relationships, but preserves the state-space representation, which is both particularly effective in several applications (e.g. optimal management and data assimilation) and facilitates the ex-post physical interpretation of the emulator structure, thus enhancing the credibility of the model to stakeholders and decision-makers. The core mechanism is a novel variable selection procedure that is recursively applied to a data-set of input, state and output variables generated via simulation of the process-based model. The approach is demonstrated on a real-world case study concerning the optimal operation of a selective withdrawal reservoir (Tono Dam, Japan) suffering from downstream water quality problems. The emulator is identified on a data-set generated with a 1D coupled hydrodynamic-ecological model and subsequently used to design the optimal operating policy for the dam. Preliminary results show that the proposed approach significantly simplifies the learning of good operating policies and can highlight interesting properties of the system to be controlled.