More than 150 million people are affected by floods, hurricanes, and other natural disasters every year, and the situation is most likely to become more serious in the coming years. The occurrence of natural disasters has quadrupled since the 1970s, majorly due to global warming. Weather, geo-spatial and previous disaster data can be used to predict to track the movement of people during a natural disaster. These insights could allow disaster relief personnel to identify how much of the supplies such as water, medical care and food will be required and where. Machine learning algorithms are able to assess the quantum of flooding, road and building damage almost immediately, based on satellite feeds and weather forecasts, thus allowing the rescuers to distribute emergency supplies more effectively and also to identify people who are still isolated from escape routes.

Autonomous robots will play an imperative role in recovery operations that are far more dangerous for human rescue teams, such as earthquakes or nuclear disasters. The only drawback is that the current robots are limited by their abilities to interact with humans effectively. The Robot Project, funded by the EU has investigated challenges faced by robots during rescue operations, and has used the findings to develop an autonomous hybrid leg-wheeled robot. The speciality of this robot is that it can adapt itself to uncertain situations, through the parallel mechanism for movement.

In case of an environmental disaster, the first 72 hours is highly critical for the rescue operation teams. Responders depend on accurate geographic information to co-ordinate operations. Drones are highly preferred as they can be deployed immediately over disaster areas, and responders can use them through 3D maps, identify victims, and assess the severity of the damages done to the infrastructure. For instance, the anticipation of hurricane Florence’s landfall in 2018 has led Precision Hawk to partner with the North Carolina Department of Transportation (NCDOT) to launch a pre-emptive UAS response. The hurricane caused damages worth US$ 20 billion by disrupting power utilities, flooding etc. The NCDOT deployed more than 250 drones and captured close to 8,000 images and videos. Using visual confirmation, state agencies were able to deploy rescue teams effectively and divert people away from high risk areas.

Further, the Kilauea Volcanic Eruption in Hawaii, in May 2018, resulted in the obliteration of more than 700 homes in Leilani Estates, Hawaii. Emergency responders flew drones over the estate to inspect the houses for damage. They were able to conduct multiple ortho flights involving several DJI multi-copters to assess the quantum of the damage caused by the volcanic eruption.

The ecosystem of AI in Disaster Management has mainly 5 tiers.

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