env.elet.polimi.it | Planning and management of environmental systems
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Research

Here is a brief description of about current research activities of the Planning and Management of Environmental Systems group. Further information can be found on the personal web pages of the people of the group.

Projects| Publications| Description of the research areas:

Air and water quality
The dynamics of air and water pollution is studied with deterministic and stochastic models both for short term prediction of quality conditions and to assess optimal long term plans for pollution reduction.

Integrated Water Resources Management
  • Modelling
    Parsimonious modelling for simulation and forecasting of water systems, namely rainfall-runoff process and distribution networks.
  • Optimal Control
    Development and implementation of methods based on Optimal Control Theory for the efficient management of multi-purpose reservoirs, reservoir networks and distribution networks (namely Stochastic Dynamic Programming, Neuro-Dynamic Programming, Reinforcement Learning). Either off-line and on-line policy design problems are adressed.
  • Decision theory
    Analysis and development of methods based on Decision and Negotiation Theory for eliciting preference structures and supporting decision-making in multi-objective, multi-DMs context and under uncertainty.
  • MODSS design
    Design and implementation of Multi Objective Decision Support Systems for integrated and participatory planning of water resources at the basin scale.
  • Ecology
  • Sustainable exploitation of ecosystems, in particular of fish resources.
    Alternative management policies are assessed under a multi-objective perspective that integrates economic, social and environmental viewpoints and accounts for environmental and demographic uncertainty.
  • Simple mechanistic models for plant-animal interactions.
    Proposal and analysis of novel mathematical models for identifying the key mechanisms that originate spatial patterns of trees in forests.
  • Model selection in population time series.
    Development and testing of new methods based on Statistical Learning Theory to distinguish between linear and nonlinear ecological models.
  • Special attention is devoted to devising robust methods for short time series.
  • Analysis of the extinction risk in fragmented landscapes.
    Habitat loss is one of the main causes of species extinction. We develop spatially implicit and explicit models to discuss the effects of different conservation policies on the probability of species persistence.

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