TRANSITION – EO-INFORMED AGENT BASED MODELS FOR DIGITAL TWINS APPLICATIONS

The TRANSITION project aims to develop and implement advanced Web Services and Applications that integrate Earth Observation (EO) and non-EO data using a sophisticated Multilevel Agent-Based Modelling (ML-ABM) system. The primary goal is to support the EU Green Deal’s vision by simulating complex socio-environmental dynamics to inform policy-making and decision-making processes. Specifically, TRANSITION focuses on optimizing land use for renewable energy production and enhancing agricultural sustainability in response to climate change. The project seeks to provide stakeholders with actionable insights into environmental policies and their impacts, facilitating the transition to a sustainable and resilient future in Europe.

Technologies and Methodological Aspects

The TRANSITION project harnesses state-of-the-art AI technologies to develop an advanced decision-support framework, focusing on optimizing land use and enhancing sustainability. Key AI-oriented technologies include:

• Advanced Machine Learning Models: The project employs cutting-edge machine learning techniques, such as neural networks and self-organizing maps, to analyze vast datasets and predict land suitability for various uses, including agriculture and photovoltaic (PV) energy production. These models process historical and real-time Earth Observation (EO) data to provide accurate and actionable insights.
• Reinforcement Learning (RL): Implemented within the Multi-Level Agent-Based Modelling (ML-ABM) framework, RL allows agents to adapt and optimize their behavior over time. Through continuous interaction with their environment, agents improve decision-making processes regarding land use and resource management.
• AI-Enhanced Geographical Information Systems (GIS): AI algorithms are integrated with GIS tools to enhance spatial analysis and visualization capabilities. This integration enables dynamic modeling of geographic data, facilitating the identification of optimal locations for renewable energy installations and assessing land suitability for various agricultural practices.
• Land Suitability Analysis: The project leverages AI and GIS to perform detailed land suitability assessments. By analyzing factors such as soil quality, climate conditions, and topographical features, the framework generates suitability scores for different land uses, aiding stakeholders in making informed decisions about land conversion and resource allocation.
• AI-Driven Predictive Analytics: AI is used to develop predictive analytics tools that forecast the environmental and socio-economic impacts of policy changes. These tools provide stakeholders with data-driven recommendations for land use planning and policy implementation.

The methodological approach combines AI with user-centered design principles to ensure that solutions meet stakeholder needs. Design Thinking (DT) and User-Centered Design (UCD) methodologies guide the creation of user stories and use cases that reflect real-world scenarios. Active engagement with stakeholders through workshops and interviews ensures the continuous refinement of AI models to meet evolving requirements.

Use Cases and Outcomes

The TRANSITION project explores several use cases, including:

1. Land Use Optimization for Renewable Energy: Analyzing the conversion of agricultural land to photovoltaic energy production and evaluating the economic and environmental benefits.
2. Green Credit Policy Simulation: Modeling the impacts of financial incentives for adopting renewable energy and sustainable agricultural practices. This use case helps policymakers understand the effectiveness of green credit schemes.
3. Climate Change Adaptation Strategies: Assessing the resilience of agricultural systems to climate impacts and exploring adaptation measures such as switching to climate-resilient crops.

Expected outcomes of these use cases include a robust decision-support system that facilitates informed investments in sustainability projects. The project aims to provide critical insights for decision-makers to evaluate green initiatives’ societal reception and effectiveness, ensuring broad support and smooth policy implementation. This approach encourages a sustainable interaction with the environment, promoting economically viable, environmentally sound practices, and socially beneficial practices.