Emerging technologies significantly influence disaster management. Innovations like cloud computing, AI, machine learning, and 5G networks enhance monitoring, response, and recovery efforts. Sensors monitor crucial parameters, while communication technologies ensure data collation and real-time communication. Big Data, AI, and ML improve decision-making, predictions, and resource allocation. Virtual and Augmented Reality, social media insights, satellite imagery, and drones provide comprehensive situational data. These technologies collectively aid in disaster prediction, awareness, mitigation, and response, emphasizing their growing importance in managing disasters.

Emerging technologies, over the past two decades have been a driving force in shaping the society. Innovations and new technologies impact the way we do business, live our daily lives and interact with each other. The exponential rise in connected devices with applications like cloud computing, wearable smart devices, artificial intelligence, machine learning, nano technology connected over super-fast 5G and satellite networks have revolutionised all spheres including disaster management. The importance of leveraging technology in disaster risk reduction is also one of the key agenda for DRR laid down by Honourable Shri Narendra Modi, Prime Minister of India. This article briefly highlights some emerging technologies and their role in disaster management.

Sensors play a very important role in monitoring important parameters before, during and after the occurrence of a disaster.  There are a wide variety of sensors like Environmental Sensors (for parameters like temperature, humidity, air quality, radiation levels etc), Seismic Sensors, Water level/Flood Sensors, Fire Sensors, Structural Health Sensors (to monitor structural health of buildings/infrastructure), Biometric Sensors to monitor vital signs and health of responders and victims), Satellite/Drone based Remote Sensors (aerial imagery and damage assessment). Wearable IoT enabled sensors provide freedom of deployment while sensors based on Nanotechnology and graphene based sensors provide ultra-precise measurements over a wide spectrum of molecules and substances. There has also been considerable development in the field of energy harvesting sensors which can be deployed and operated with minimum supervision in remote areas. Machine learning the algorithms feeding on an array of sensors have higher accuracy and predictive capabilities by learning pattern and anomalies of the collected data.

Emerging communication technologies have also impacted collation and backhaul of data. It is now easier for IoT enabled sensors to network and aggregate data which can then be backhauled over satellite connectivity. IoT’s ability to connect devices, gather data, and enable real-time communication plays a critical role in enhancing disaster preparedness, response, and recovery efforts. Ultra-fast data transfer speeds of 5G Networks enable quick sharing of large datasets such as high-resolution images, videos, and real-time sensor data, which is crucial for informed decision-making for every stage of Disaster Management.  Low latency and high bandwidth of 5G networks enable seamless, high-quality communication between emergency responders, agencies, and affected communities, facilitating rapid response coordination by supporting real-time collaboration between experts and responders. Satellite based Internet is also poised to extend the reach of Internet making its availability global thereby greatly enhancing connectivity for data gathering and alert dissemination devices. The enhanced reach of the internet over satellites will also ensure that there is no disruption of communication after a disaster. Satellite-based internet provides a lifeline during disasters, ensuring connectivity, information exchange, and efficient response coordination, ultimately contributing to more effective disaster management and recovery.

Technological advancements in Big Data Analytics, Artificial Intelligence, Machine Learning and Predictive Analytics are driving Decision Support Systems to empower disaster managers during various phases of the disaster management cycle. Data analytics of very large volumes of diverse data using AI algorithms and ML models have led to a better analysis and predictions for faster resource allocation and response. Historical data, response and the post hazard damage assessment can be analysed by AI algorithms more accurately. AI algorithms can consider factors like road closures, traffic patterns, and available emergency services to optimise resources and determine the quickest and most effective response routes. Machine learning helps to process data from various sources to estimate the scale of damage, which aids in estimating recovery efforts and required resources. Satellite imagery and drone footage processed by Machine learning algorithms provide realistic extent of damage and help responders to prioritize rescue operations.  Predictive analysis tools use both AI and ML by using advanced algorithms to process vast amount of historical data, in order to anticipate and prepare for potential disasters. The vulnerability index of regions based on physical, social, economic, and environmental factors can be evaluated by predictive analysis in order to optimize the prepositioning of resources and initiate mitigation measures. A Decision Support System is a confluence of AI, ML, Predictive Analysis and Big data analytics and it negates cognitive human limitations by providing decision options based on sound scientific analysis and deductions.

Virtual and Augmented Reality offers immersive and interactive experience. It adds a realistic and interactive dimension to training, simulation, situational awareness and decision making by first responders. Digital information like maps, evacuation routes, and resource locations can be provided as overlays to live imagery thereby enhancing situational awareness of responders with crucial data. Real time Satellite imagery can also be enriched by VR and AR driven algorithms to create 3D models of disaster-affected areas. Such models can assist disaster managers in assessing damage and planning recovery efforts.

With the proliferation of the Internet and Social Media, there is a large amount of information available online which can assist disaster managers. Social media platforms provide excellent real time crowd sourced inputs of any developing situation pertaining to disasters. This data includes photos, videos, and eyewitness accounts from affected individuals. Analysis of this vast amount of data using Big data Analytics opens new avenues of helpful information for disaster management in terms of early signs of disasters and emerging incidents. These inputs can assist in identifying areas which need immediate assistance. Geo-tagged data acquired from social media can also provide valuable information for identifying individuals, communities and regions which need immediate help.

Satellite Imagery and Aerial Photography from Drones and Unmanned Aerial Vehicles (UAVs) provide timely and accurate information to assess, respond to and recover from disasters. Remote sensing by such non-contact methods have the inherent advantages of global reach and survivability in the aftermath of a disaster. Visual data of disaster-affected areas helps authorities to assess the extent of damage to infrastructure, buildings, and the environment. Drones and UAVs equipped with cameras, thermal sensors, and other imaging technologies can be launched quickly to search large areas, identify victims and assist in navigation of rescue teams. In addition of flexibility in terms of deployment, remote imagery and other sensor data collected by Drones and UAVs has higher resolution. Quick decision making to allocate resources, identification of areas which need immediate response and planning of routes for first responders can be greatly assisted by real-time imagery of the effected region. Remote sensing data can also be used to identify areas prone to disasters, assess vulnerability, and develop preparedness plans.

Technology has a great role to play in the dissemination of Alerts to forecast impending Disasters. Timely dissemination of Alerts to the targeted populace is a very important factor in ensuring preparedness thereby reducing loss of lives and property. Common Alerting Protocol which has now been adopted world- wide has standardised Alert information in a defined format for easier and faster processing. Alerts from all Alert generation agencies can be aggregated and thereafter have a single authorised source of Alerts in the country. Such Alerts can be geo targeted in regional vernacular and pass vital information as well as instructions during various phases of Disasters. Alerts can be disseminated over Mobile Networks to Mobile phones and all connected devices by geo targeted Cell Broadcast and SMS. Terrestrial, DTH and Cable TV broadcasts can also visually display Alert information on an overlay to any channel being viewed. Similarly Radio broadcast network comprising of public and private AM, FM, community radio and internet radio can also be harnessed to relay alerts to their subscriber base. Availability of Alerts on a standard CAP format also makes it easy for any departmental information dissemination system to add the functionality of Alert dissemination. An example of such an arrangement is integration of Railway passenger information system with CAP alert aggregation platform. A versatile and customisable Mobile Application integrated with the CAP platform will also play a vital role in the dissemination of Alerts.     

Deployment of emerging technologies for disaster management helps in the scientific analysis of causes for disasters, thereby monitoring and predicting such events. Technology also helps us in taking measures like increasing disaster awareness and timely early warning to reduce the impact of disasters on people, communities and infrastructure. Emphasis on emerging technologies will continue to play an important role during the phases of preparation, response, recovery and mitigation during disaster management.