The research aims at developing mathematical models for the quantitative analysis and management of environmental systems at both global and local scales. Embedding these models into modern computer-based decision support tools allows to process large amounts of environmental and territorial data and to evaluate any control action before being implemented, improving efficiency and reducing risks. To this purpose, such models must describe both the natural dynamics of the environmental systems and the decision framework, which is often very complex. This will ultimately support a more sustainable use of resources and will make decision processes more transparent and shareable.

Reforestation of Peruvian Amazon
A model with multiple objectives has been studied with the purpose of safeguarding the great ecological richness of the Amazon area, but at the same time allowing a productive activity to the local communities. It highlights the interrelations between some classic sustainability indicators: biodiversity, economic performance and social acceptability. The model has shown that the criteria normally used for agricultural planning cannot be applied to the case of reforestation because annual crops and tall trees must coexist on the same land and therefore the growth dynamics of different species must be considered over plurennial time horizons.

Modeling the dynamics of infectious diseases
The study of spatiotemporal dynamics of infections in human, animal and plant populations allowed the development of innovative modelling of disease spread via transmission networks of different kinds, ranging from hydrological to human mobility or cattle trade networks. These models have been applied to the epidemiology of cholera and schistosomiasis, and the potential spread of veterinary infections in farming and aquaculture and to the analysis of the effects of different intervention policies.

Conservation and management of marine ecosystems
The development of oceanography-driven models of propagule and organism dispersal in the marine environment  allowed a better understanding of connectivity within the seascape, and an optimization of the spatial planning of conservation actions, and sustainable management of fisheries. The focal areas of interests were the Mediterranean sea, and the Pacific and the Atlantic Oceans.  

Residential water demand modelling and management
The study developed end-use disaggregation algorithms for high resolution smart-metered water (and energy) consumption data. The application of big-data analytics tools allowed a detailed users’ profiling and customer segmentation to mine typical water consumption patterns. This allowed the design of customized residential water demand management strategies.

Robust water resources planning and control
The analysis and generation of future climate and socio-economic scenarios led to the development and application of multi-objective optimization and control algorithms for discovering flexible and robust water management solutions to address the main drivers of vulnerability of the system.