Scenario data dashboard
Kassandra Srl
4. Predictive Scenarios
Scenario comparison dashboard
Kassandra Srl
Scenario comparison on Digital Twin
Kassandra Srl
Kassandra is a predictive system, and it does so by creating ‘scenarios’ in which key parameters are altered individually or collectively and the variation of the Resilience Index is calculated. This is done iteratively until an optimum level is reached.
In addition, the scenarios can be of two types, passive and active. Passive scenarios are those where parameters external to the system are altered, for instance climatic data, whilst active scenarios simulate actual adaptations or management strategies, such as extensive tree planting.
The scenarios are not a forecast but plausible alternative images of how the future can unfold, or, as defined by the IPCC - Intergovernmental Panel on Climate Change.
Key conditions include:
- Flexible Parameter Adjustment: The ability to easily alter key parameters, both individually and collectively, is crucial for exploring various scenarios and their impacts on the Resilience Index.
- Comprehensive Scenario Planning: Implementing a structured approach to scenario planning helps ensure that all relevant variables are considered in the analysis.
- Real-Time Data Integration: Incorporating real-time data feeds allows for dynamic scenario adjustments, improving the relevance and accuracy of predictions.
- Stakeholder Input: Involving stakeholders in defining scenarios ensures that they reflect real-world concerns and priorities, enhancing buy-in and applicability.
- Importance of Accurate Models: Initial models that lacked precision led to unreliable scenario outcomes. Ensuring data models are validated and refined improves prediction quality.
- Parameter Interdependencies: Altering parameters individually sometimes yielded unrealistic results. Recognizing and accounting for interdependencies among parameters enhances scenario realism.
- Iterative Testing: Conducting iterative tests of scenarios helped identify flaws and areas for improvement. Early iterations often revealed unforeseen implications of parameter changes.
- Stakeholder Engagement: Gathering input from stakeholders in defining scenarios was crucial. Scenarios that did not align with community concerns faced challenges in acceptance and implementation.
- Clear Communication: Presenting scenario results clearly and visually improved understanding among stakeholders. Complex data without clear visualizations often led to confusion and misinterpretation.