Disasters and emergencies management requires four main phases. Mitigation phase is the first phase of the crisis management cycle. It basically aims to mitigate the danger or even to avoid the crisis. Understanding and anticipating crisis occurrence are key activities to succeed this phase. Snow avalanches are one of the natural disasters that affects several countries in the world. In this paper, we study and discuss the impact of land characteristics and snow stability on avalanche occurrence and severity level. We introduce a Machine learning framework for avalanches forecasting and we use a real dataset to determine prominent factors that can predict the class/severity of an avalanche. To achieve our goal, we apply a Multi-Layer Perceptron (MLP) and we determine a feature subset which achieves the highest accuracy

Emergency and crisis management is a constantly changing field that needs a lot of attention. In order to save lives and lessen the effects of crises, numerous response organizations work together to ensure the successful execution of various phases. Organizations and managers must act quickly and wisely due to the urgency of the situation. The gathered data and available expertise play a major role in how well these decisions turn out. The quality and applicability of the information gathered, which comes from various sources like satellite imagery, local sensors, and social media produced by the local population, is intricately linked to the decision-making process. Achieving effective crisis management requires the capacity to swiftly analyze and interpret the data at hand, accurately assess the circumstance, and render well-informed decisions based on knowledge and accepted practices. The communication, representation, and integration of heterogeneous information are difficult tasks for these processes. Ontologies play a critical role in facilitating decision-making processes in the fields of artificial intelligence and computer science. By offering a formal framework and a common vocabulary for representing knowledge within particular domains, they act as priceless tools. In this article, we propose a comprehensive crisis management ontology that effectively encompasses the fundamental concepts associated with this field. This ontology facilitates the efficient management, organization, and comprehension of information, leading to improved streamlining of processes.

Snow avalanches is considered among the most dangerous natural disasters that cause material damage but also dozens of deaths every year. The concerned countries have allocated important resources to manage this disaster. Space-time avalanche observation and prediction can help save people's lives and minimize damages. In this context, this article provides a comprehensive insight on existing avalanche forecasting systems. We review observed natural phenomenons and parameters that most existing systems use, then we discuss existing methodologies for snowpack modeling. We establish a comparative study on most important methodologies and tools for avalanche prediction. Finally, we give some recommendations and discussion for building an operational prediction system.