By Fateen Naqash
Disasters, be they natural or caused by humans, are widely recognized as events that affect
communities profoundly by causing loss of life and damaging infrastructure, with lasting economic
repercussions. With time, advancements have been remarkable in the field of technology, offering
promising opportunities to enhance disaster precautions and response efforts in countries susceptible
to calamities. Artificial Intelligence, commonly referred to as, AI, can be used to predict upcoming
disasters with high accuracy which in turn can help us prepare, respond and manage the losses that
follow effectively.
Kashmir, while being called “The Switzerland of the East” (Akins, 2023), is very susceptible to
disasters. The integration of AI in disaster management of the state holds incredible potential.
Earthquakes, avalanches, landslides, are some of the disasters Kashmir has had to face over the
previous decades. In this article, we will explore the prior natural disasters that have befallen the
beautiful land of Kashmir, how AI could have made a significant difference if it were to exist back then,
and how it can forever change how the citizens of Kashmir deal with the consequences of such
disasters.
Understanding the Role of AI in Disaster Management
Artificial Intelligence is just a term that consists of the utilization of machine learning algorithms,
robotics, computer vision and other technologies to analyze and process data to make predictions.
These predictions can be extremely precise when abundant data, combined with an appropriate
approach to training the model, is taken. AI can analyze data way faster than humans can, due to
which the prediction, optimization and decision-making can happen in real-time during emergencies.
The use of AI in disaster management can be categorized into several key functions: prediction and
forecasting, damage assessment, resource management, decision support systems, and recovery
and rehabilitation. Each of these functions can be critical in ensuring a faster, more coordinated, and
efficient disaster response.
AI in Predicting and Forecasting Disasters
AI is becoming an essential tool in forecasting and managing natural disasters. By utilizing data from
multiple sources such as satellite imagery, weather reports and historical disaster records, predictive
models can be developed that assess the likelihood of future events. In flood forecasting, these models
can analyze trends in rainfall, water levels, and land topography to predict potential flood risks. In
regions like Kashmir, which is prone to seismic activity due to their location on the Himalayan fault
lines, AI can enhance earthquake prediction efforts. By monitoring data from seismic sensors, machine
learning algorithms can detect subtle patterns in ground movements, providing timely alerts to help
minimize the impact of such disasters.
For instance, in 2014, Kashmir experienced a devastating flood that had severe consequences. The
floods occurred due to the continuous rainfall and the overflow of the Jhelum River. Over 2,600 villages
were flooded and over 200,000 structures were damaged (Malik & Hashmi, 2021). AI could have been
used for better disaster prediction and management. If they were readily available at the time, AI-
based systems could have calculated the high-risk zones accurately, allowing authorities to evacuate
such areas ahead of time and minimizing casualties exponentially.
AI in Damage Assessment and Situational Awareness
During and after a disaster, the immediate priority is to assess the extent of the damage. AI offers a
fast and efficient way to process and analyze large quantities of data, such as satellite images or drone
footage, to provide a clearer picture of the affected areas.
The 7.6 magnitude earthquake that hit Kashmir in 2005, killed nearly 75,000 people, injured more than
100,000 and destroyed 3 million homes (NASA, 2008). Relief teams faced challenges in determining
which areas were most affected and where the survivors were located. Machine learning models,
specifically those used in computer vision, could have been of great assistance to recognize collapsed
buildings and inaccessible regions, which would accelerate the response process.
AI’s ability to process data in real-time also means that relief operations can be more targeted and
efficient. For example, the data from the sensors placed directly in the ground along with aerial
surveillance can be used to pinpoint the exact location of survivors, directing rescue teams to the most
critical areas first. In the case of Kashmir’s Avalanche of 2017, where dozens of lives were lost, drones
equipped with AI could have been used to map out snow patterns and assess areas where the
avalanche caused the most destruction, guiding rescuers in a more efficient manner.
Optimizing Resource Allocation with AI
The effective distribution of resources, including food, medical supplies, personnel, and equipment, is
essential in any disaster response. By anticipating which regions will require the most supplies and
allocating resources appropriately, AI can assist in streamlining these procedures. AI in supply chain
management minimizes waste and increases the effectiveness of relief efforts by ensuring that
resources reach the most vulnerable areas. Furthermore, by simulating supply chain dynamics, AI
platforms can optimize the distribution of goods and services. The use of AI in supply chain
management ensures that resources are delivered to areas that are most at risk, reducing waste and
maximizing the impact of relief efforts.
Moreover, AI platforms can optimize the distribution of goods and services by modeling supply chain
dynamics. For instance, drones and autonomous vehicles can be used to deliver supplies to areas
where traditional transportation is blocked due to floodwaters or landslides. AI systems can
continuously adjust resource distribution based on evolving conditions, ensuring an agile and
responsive relief operation.
AI in Real-time Decision Support Systems
Disasters frequently need making snap decisions in stressful situations. By offering real-time insights
and suggestions based on an ongoing stream of data, artificial intelligence (AI) can assist in decision-
making. To provide useful insights, these systems can combine information from social media reports,
seismic activity, weather forecasts, and emergency services.
AI can also assist in forecasting the possible outcomes of particular acts, including dam openings or
closures or population evacuations. AI-powered decision support systems can provide a variety of
scenarios and results, helping decision-makers make the best decisions under duress.
The Future of AI in Kashmir’s Disaster Management
Implementing the AI system in disaster management in Kashmir together with the government
agencies, technology providers and international aid organizations. Also, investment would need to go
into infrastructure and things like low-Earth orbit satellite communication technologies, ground-based
sensors and drones through which environmental data would be gathered. Even though there are
challenges, the lifesaving and monetary returns possible with AI are astonishing.
The future of disaster response in Kashmir, could involve AI-driven systems that not only forecast
natural events but also learn adaptively, overcoming some of the current challenges by learning from
every disaster to inform all subsequent crises. These systems can make Kashmir disaster resilient
and protect its citizens from damage.
Ishfaq Hussain Malik, S. Najmul Islam Hashmi; The Great Flood and its Aftermath in Kashmir Valley:
Impact, Consequences and Vulnerability Assessment. Jour. Geol. Soc. India 2021;; 97 (6): 661–669.
doi: https://doi.org/10.1007/s12594-021-1742-y
Akins, H. (2023). “Jammu and Kashmir: ‘The Switzerland of the East’”. In Conquering the maharajas.
Manchester, England: Manchester University Press. Retrieved Nov 7, 2024,
from https://doi.org/10.7765/9781526167866.00010
Naranjo, L. (2008, October 22). When the Earth moved Kashmir. NASA.
https://earthobservatory.nasa.gov/features/KashmirEarthquake
fa***********@***il.com