Towards to Integrate a Multilayer Machine Learning Data Fusion Approach into Crisis Classification and Risk Assessment of Extreme Natural Events

Nowadays, one of the most critical challenges is the ongoing climate change, which has multiple and significant impacts on human life in financial and environmental levels. As the adverse effects of unexpected destructive natural extreme events, such as loss of human lives and property, will become more frequent and intensive in the future, especially in developing countries, the efficient confront is required in a holistic manner. Hence, there is an urgent need to develop novelty tools to enhance awareness and preparedness, assess risks and support decision-making, aiming to increase social resilience to climate changes. This work suggests a unified multilayer framework that encapsulates machine learning techniques in the risk assessment process for analysing and fusing dynamically heterogeneous information obtained from the field.