In the ever-evolving landscape of disaster management and remote sensing, a groundbreaking tool is emerging that promises to revolutionize how we monitor and respond to natural hazards. Developed by Robiah Al Wardah from the Department of Geological Sciences and Geological Engineering at Queen’s University in Canada, the Natural Disaster Information System (NDIS) is a geospatial decision-support framework designed to optimize the use of Remotely Piloted Aircraft Systems (RPAS) for natural hazard missions. This innovative system is not just a technological marvel but a potential game-changer for industries, including the energy sector, that are increasingly vulnerable to the impacts of natural disasters.
The NDIS integrates a global geohazard database with the specifications of various RPAS platforms and sensors, automating mission planning in a way that was previously unimaginable. “The system uses advanced algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility,” explains Al Wardah. This means that whether it’s a volcanic eruption, an earthquake, or a flood, the NDIS can quickly and accurately determine the best tools and methods for monitoring and mitigating the disaster.
The evolution of NDIS from version 1.0 to the latest version 3.8.3 showcases a remarkable progression in technology and functionality. The initial version used decision tree algorithms, while subsequent versions introduced more sophisticated methods like the Random Forest method and the Critical Path Method (CPM) to optimize task sequencing and mission timing. The latest version implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS.
One of the most compelling aspects of the NDIS is its user-friendly interface and additional features. The system supports both batch processing and interactive real-time queries through a web-based user interface. It also includes a statistical overview dashboard to help users interpret dataset distribution and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. This makes the NDIS not just a tool for experts but a resource that can be utilized by a broader range of stakeholders, including those in the energy sector.
The implications of this research are vast. For the energy sector, which often operates in areas prone to natural disasters, the NDIS can provide critical insights and real-time data to enhance safety and operational efficiency. “This system can help energy companies to better plan and execute their operations, reducing the risk of disruptions and ensuring the safety of their personnel and infrastructure,” says Al Wardah.
The NDIS is not just a theoretical concept; it has been validated in real-world applications, such as monitoring volcanic hazards in Indonesia. This practical validation underscores the system’s potential to be a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning.
As we look to the future, the NDIS represents a significant step forward in the field of disaster management and remote sensing. Its ability to integrate advanced algorithms, user-friendly interfaces, and crowdsourced data makes it a powerful tool for a wide range of applications. For the energy sector, this means enhanced safety, improved operational efficiency, and a more proactive approach to managing natural hazards.
Published in the journal ‘Drones’ (translated to English as ‘Bees’), this research is set to shape future developments in the field, offering a glimpse into a future where technology and innovation converge to create safer, more resilient communities and industries.