• Natural or human-made disasters have a devastating effect on the local population as well as the environment. DL in disaster management helps in mitigating such effects.
Deep learning (DL) is a technology that needs no introduction. The technology has left everyone astonished due to its path-breaking innovation across industries. But the application of technology to environment is a different ball game altogether. Disasters – natural or human-made – result in tremendous loss of life and property. According to a report on global disasters:
- In the last 20 years, we lost 1.35 estimated million lives to disasters
- For post-disaster recovery, we’ve lose approximately 300 U.S. dollars per year
Such substantial loss of life and property makes us question the current disaster management model. What’s not working well with it? Why isn’t it providing optimized results? The failure of these models to forecast the occurrence of disasters and to manage the post-disaster recovery has created the necessity to implement an advanced technology like DL in disaster management.
Challenges with the current disaster management model
The current disaster management approach involves satellites and drones to gather data from areas are prone to disasters. But how effective are the results of this approach? The current disaster model fails to:
- offer data in real time,
- gather data from multiple sites at the same time, and
- sugegst proactive steps for disaster prevention
Additionally, the current disaster management systems do not offer clear and crisp images of disaser prone regions. This is why, it’s high time experts implement DL in disaster management to overcome these current issues.
Deep Learning (DL) in disaster management
Data from multiple sources, such as weather reports, satellite images, disaster history, can be used to train a DL system. After the training, DL can foretell the occurrence of a disaster using a convolutional neural network. With insights drawn from thorough analysis, experts can predict the imminent occurrence of disasters, helping experts and people to follow a proactive approach and minimize the devastation otherwise expected. No one can pause or halt the occurrence of natural or human-made disasters. We can only take steps to reduce their impact as far as possible. By integrating DL applications with drones, experts can get real-time data on areas that are about to get hit by a disaster. Timely and sufficient measures can then be taken to save lives and property. Furthermore, drones can also track specific areas, such as forests and narrow geographical locations, so that special help can be extended to such difficult terrains. Gone are the days when only product-oriented, commercial fields planned to use DL. Today, DL is ready to show its potential in unconventional areas like disaster management.