  The impacts of mass migration, such as crisis induced by climate change,
extend beyond environmental concerns and can greatly affect social
infrastructure and public services, such as education, healthcare, and
security. These crises exacerbate certain elements like cultural barriers, and
discrimination by amplifying the challenges faced by these affected
communities. This paper proposes an innovative approach to address migration
crises in the context of crisis management through a combination of modeling
and imbalance assessment tools. By employing deep learning for forecasting and
integrating causal reasoning via Bayesian networks, this methodology enables
the evaluation of imbalances and risks in the socio-technological landscape,
providing crucial insights for informed decision-making. Through this
framework, critical systems can be analyzed to understand how fluctuations in
migration levels may impact them, facilitating effective crisis governance
strategies.
