Data to Decision in Natural Disaster Management

Information is a critical part in the organisation and execution of work in civil protection work. First responders such as firefighters collect information on emergencies to organize their work prior and during natural disasters. From traffic accidents, forest fires, floods, to rescue operations and inspections of buildings. To prepare for forest fires, climate data, terrain data, type of incident, and cause of fire are collected. The statistics are used to identify various risks that cause incidents, try to mitigate accidents, and to train and prepare appropriate responses when fires occur. With advancements in technology, remote sensing and data collection technologies can monitor, collect and analyze data efficiently to support the decision-making process prior, and during emergencies. The TEMA project will utilize existing data sources, supplemented with additional sources to create models on the further progress of forest fires and floods. The information will be presented to the disaster response team through augmented reality glasses and a desktop interface.

Big data

Preparation and responses to natural disaster are made from a combination of historical and live data. Policies on prevention and preparedness are built from historical data. New data is observed as the risk for forest fire rises. Due to the unpredictability of natural disasters and limited knowledge of the extent of the fire, it is difficult to plan appropriate resources in advance (Yu et al., 2018). With more information collected and analysed from the disaster site on the scale and propagation of the disaster, first responders could mitigate the extent of the damage. The large amounts of data available require collection and processing, analysis and visualization for the disaster management personnel. 

TEMA will make use of big data collected through existing services providing data on climate, terrain, social media activity and supplement in with satellite observations, UAV images and videos and remote sensors. The many sources and types of data will be analysed and used to detect fires and floods, and to provide models on fire and flood evolution/propagation. To visualize the real time situation, visualisation for augmented reality glasses and a desktop interface will be used. This TEMA natural disaster management platform will utilize AI for the monitoring of the disaster situation and provide support in decision making.

Big data and natural disaster management

To structure work prior, during and after an incident, natural disaster management is structured into four stages: risk reduction, preparedness, response, and recovery (Sakurai & Murayama, 2019). The TEMA platform therefore has the potential to address the four stages of disaster management for the work of firefighters. In risk reduction, remote sensing devices monitor infrastructure and the environment, while UAV’s provide images, videos and object detection. During preparedness, first responders and citizens build risk awareness and resilience. With more information on the situation, disaster response and civil protection can be coordinated efficiently. During recovery, emergency responders report on the incident, identify resource allocation and vulnerabilities to improve for future actions.

Climate change is increasing the risk of large-scale fires across the north of Europe. The Fennoscandian area is expected to warm faster than the global average. The increasing drought periods will contribute to longer seasons with high risks of fire (Venäläinen et al., 2020). Given the low population density and large distances between population centres, timely and precise information is critical in decision making to reduce the extent of the fire damage before it is extinguished. 

In Finland forest fire incidents are expected to occur more frequently over the next decades.  Identified challenges and solutions have been structured into five categories (Finnish Meteorological Institute et al., 2021):

1. Developing control and modelling 

This measure includes the adoption of fast and accurate fire detection technologies and ways to automate the alarm system. Fire propagation models are required to assist in the development of firefighting strategies. Adoption of new technologies such as drones.

2. Development of equipment and extinguishing technology 

This measure includes the adoption of extinguishing equipment suitable for challenging forest fires, and development of situation and command center operations as well as resource sharing.

3. Taking care of sparsely populated areas

Sparse areas require contract fire brigades, and solutions to offset the demographic decline, leading to reduced fire detection and need for off road maintenance. Finally, identification of the risks of nature tourism. 

4. Cooperation

Adequate firefighting equipment and sharing of resources on a national level. Nordic and EU cooperation and sharing of know-how between the forestry and rescue services sectors.

5. Strengthening outdoor skills

Reduction of forest fire risks through education. Public safety communication through schools, to tourists and entrepreneurs. Awareness raising of risks, as well as guidelines for visitors.

In line with the identified challenges and solutions, the Kajaani pilot on forest fires will trial the TEMA solution in addressing the needs for natural disaster management in Finland. In conclusion, the TEMA project will develop and demonstrate the use of big data to support natural disaster management. Civil protection organisations from Italy, Greece, Germany and Finland contribute through the definition of end user requirements and piloting activities in flood and fire disasters.

References

• Finnish Meteorological Institute, Aalto, J., & Venäläinen, A. (2021). Climate change and forest management affect forest fire risk in Fennoscandia. Finnish Meteorological Institute. https://doi.org/10.35614/isbn.9789523361355
• Sakurai, M., & Murayama, Y. (2019). Information technologies and disaster management – Benefits and issues -. Progress in Disaster Science, 2, 100012. https://doi.org/10.1016/j.pdisas.2019.100012
• Yu, M., Yang, C., & Li, Y. (2018). Big Data in Natural Disaster Management: A Review. Geosciences, 8(5), 165. https://doi.org/10.3390/geosciences8050165
• Venäläinen, A., Lehtonen, I., Laapas, M., Ruosteenoja, K., Tikkanen, O., Viiri, H., Ikonen, V., & Peltola, H. (2020). Climate change induces multiple risks to boreal forests and forestry in Finland: A literature review. Global Change Biology, 26(8), 4178–4196. https://doi.org/10.1111/gcb.15183