Situational Awareness Technologies Best Practices
Posted March 1, 2021 by Soichi Hayashi
Sensors and Brains(AI)
A new generation of digital technologies is helping governments take decisive control over major disaster events. Perhaps the most important development in flood-response technology is the rise of situational awareness platforms.
This technology enables decision-makers to effectively coordinate response efforts at a moment’s notice, rather than executing strategies designed for dynamic situations that will almost certainly have changed by the time first responders arrive on the scene.
Situational awareness platforms integrate discrete technologies, synthesize information streams, and activate data from UAVs, intelligent infrastructure, meteorological data, and more. Such integration helps responders build a common operational picture, enabling crews on the ground to execute their critical responsibilities with far greater effectiveness.
AI / Big Data
An avalanche of data is being generated by sensors, closed-circuit television, smartphones, financial transactions and Internet activities, to name just a few. While many of these data are being mined by businesses for commercial purposes, Big Data analytics holds enormous potential for crisis management
AI could have a tremendous impact for disaster management regarding quickening recovery and response times.
Considerable research is currently being devoted to the use of AI for detecting and possibly one day predicting earthquakes.
Humanitarian groups are hoping to speed up map creation by using machine learning to extract objects such as buildings and roads from aerial images.
Another example is the use of financial transactions to monitor economic activity during and after a disaster, in order to improve targeting of support efforts.
Researchers used Big Data techniques to explore financial transactions before, during and after Hurricane Odile struck the Mexican State of Baja California Sur in September 2014, to analyse its economic impact on those affected. The analysis identified which groups were most affected for targeting post-disaster assistance and how long it took to return to normal, and generated estimates of the economic impact. It would be attractive to develop an ex ante model so that the financial response to disasters could be analysed in real time to provide ongoing feedback loops to relief efforts.
The use of sensors for monitoring conditions that could trigger disasters dates back a number of years. Improvements in cloud computing, broadband wireless networks, the sensors themselves and data analysis have led to the emergence of powerful, integrated and real-time systems referred to as the Internet of Things (IoT).
Disaster management is an ideal use case for IoT applications, since sensors can send alerts about a number of potentially dangerous situations.
Tree sensors can detect if a fire has broken out by testing temperature, moisture and carbon dioxide levels.
Ground sensors can detect earth movements, which might signal earthquakes.
River levels can be monitored by sensors for possible flooding.
– For each cell, score 1) importance/effectiveness 2) maturity of the technology 3) cost/benefit?
|Disaster||Above Group Drones||Underwater Drones|
|Flooding / Surveillance||10||5|
|Flooding / Mapping||10||5|