NEXOGENESIS

Next generation of effective and intelligent water-related policies using Artificial Intelligence and reinforcement learning to assess the water-energy-food-ecosystem (WEFE) nexus.

The NEXOGENESIS project aims to support the development of policies that manages resources effectively. To do so, the project is developing and validating a coherent cross-sectorial policy-making framework at different scales, addressing climate and socio-economic change, as well as stakeholder behaviour and transboundary (diplomacy) issues. This solution is developed for and validated by stakeholders, policy makers and academics.

Using Artificial Intelligence and Machine Learning techniques, a Self-Learning Nexus Assessment Engine (SLNAE) will be developed to support the streamlining of water-related policies into the WEFE nexus. This engine will enable users to explore potential impacts of nexus-related policies in different climate and socioeconomic future scenarios to help them develop optimal policies. Additionally, a Nexus Footprint indicator monitoring the status of nexus components will track the progress of policy objectives.

Water, energy, food and ecosystems (WEFE) form an interconnected nexus, key in providing resources and services needed to sustain human activity, which is affected by climatic and socio-economic changes. Resource constraints, and their interconnectedness could hamper economic development, including optimal trade, market and policy solutions. Moreover, policy objectives tend to be developed with little consideration to multiple sectors, and policy implementation may change nexus characteristics, in turn affecting actor behaviour and policy formulation.

NEXOGENESIS project is participated by the Applied Artificial Intelligence Unit of Eurecat which is leading the Data Science Work Package in which novel AI techniques are applied to investigate and promote efficient WEFE policies through the design and development of the Self-Learning Nexus Assessment Engine, a software ecosystem based on Deep Reinforcement Learning agents.

The consortium of the project, coordinated by the Delft Institute for Water Education (IHE), counts with the participation of 20 European partners.