
In February 2025, Ltf200 developed two core features for the GEB-model/GEB repository, focusing on agent-based simulation for household flood risk adaptation. They implemented a configuration loader for household agents, introducing migration defaults and step function termination logic to streamline lifecycle control. Ltf200 also designed a modular flood adaptation decision framework, enabling simulation of strategies such as adapting, insuring, or migrating based on flood-risk data. Using Python, Numba, and YAML, they validated end-to-end workflows with dummy data and emphasized modularity and traceability. The work established a robust foundation for scenario-based resilience planning and automated risk assessment without reported bugs.

February 2025 (2025-02) monthly summary for GEB-model/GEB. Key deliverables include: (1) Households agent configuration loading with migration defaults, including default migration config and step function termination logic to simplify lifecycle control; (2) Flood adaptation decision framework with utilities to evaluate adapting, insuring, doing nothing, and migrating, plus household-level simulation capabilities using flood-risk data. No major bugs reported this period. Impact: enables scenario-based resilience planning, risk-informed decision making, and streamlined configuration management; supports automated workflows and faster time-to-insight for household risk analysis. Technologies/skills demonstrated: modular decision-module design, utility-based decision calculations, end-to-end workflow validation with dummy data, step function integration, and solid commit traceability.
February 2025 (2025-02) monthly summary for GEB-model/GEB. Key deliverables include: (1) Households agent configuration loading with migration defaults, including default migration config and step function termination logic to simplify lifecycle control; (2) Flood adaptation decision framework with utilities to evaluate adapting, insuring, doing nothing, and migrating, plus household-level simulation capabilities using flood-risk data. No major bugs reported this period. Impact: enables scenario-based resilience planning, risk-informed decision making, and streamlined configuration management; supports automated workflows and faster time-to-insight for household risk analysis. Technologies/skills demonstrated: modular decision-module design, utility-based decision calculations, end-to-end workflow validation with dummy data, step function integration, and solid commit traceability.
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