How AI is being used to predict disasters?
Theme: The occurrence of disasters could be lessened by anticipating them in
advance. Researchers are
utilizing artificial intelligence to predict natural disasters with high
accuracy. By foreseeing these events, we have the potential to save
countless lives and implement suitable measures to minimize property
damage.
Summary:
Artificial intelligence has proven beneficial in diverse fields, including customer service, trading, and healthcare. Remarkably, researchers have also discovered its potential to forecast natural disasters. Utilizing vast, high-quality datasets, AI can accurately predict the onset of various natural calamities, potentially saving thousands of lives. This capability could be a critical factor between life and death for those affected by such events.
Earthquakes
Deep learning systems are being employed by researchers to analyze massive amounts of seismic data related to earthquakes. Artificial intelligence is proving to be highly effective in examining earthquake magnitude and patterns, and this data holds the potential for predicting future earthquake occurrences. A notable example involves Google and Harvard collaborating on an AI system specifically designed to forecast earthquake aftershocks. By leveraging a neural network built on over 131,000 earthquake and aftershock data points, the system outperformed traditional methods in predicting aftershock locations with greater precision.
Beyond this collaboration, multiple researchers are developing their own applications for earthquake and aftershock prediction. The envisioned future includes the ability to foresee earthquakes and execute evacuation operations based on such forecasts. Currently, Japan is utilizing satellite imagery analysis to predict natural disasters, with AI-based systems searching for changes in these images to identify risks like earthquakes and tsunamis. Additionally, these AI systems monitor the condition of aging infrastructure, detecting deformations in structures that can help mitigate damage from collapsing buildings, bridges, and subsiding roads. The combination of AI-powered earthquake prediction and infrastructure monitoring holds promise for reducing the impacts of seismic events and ensuring greater public safety.
Floods
Google is in the process of creating an AI platform to anticipate floods in India and issue warnings through Google Maps and Google Search. This AI system is trained using rainfall records and flood simulations as data sources. Similarly, researchers are also working on AI-driven systems that learn from rainfall and climate data, tested alongside flood simulations, yielding superior flood prediction compared to traditional methods. Additionally, AI can be harnessed for monitoring urban flooding. At the University of Dundee in the United Kingdom, researchers are collecting crowd-sourced data from platforms like Twitter and mobile apps to monitor urban flooding. The data includes images and location-specific information, which is then analyzed by AI algorithms. These systems have the potential to predict and monitor flood damage, complementing other existing approaches. Moreover, artificial intelligence and deep learning applications hold significant promise in the realm of disaster management.
Volcanic Eruptions
In the past, researchers faced challenges in accurately predicting natural disasters like volcanic eruptions. However, a breakthrough has emerged as scientists are now employing AI to identify minute ash particles originating from volcanoes. The shape of these ash particles enables the identification of the specific type of volcano. This advancement holds significant promise for enhancing eruption predictions and developing effective techniques to mitigate volcanic hazards.
IBM is also at the forefront of developing Watson, an AI system that utilizes seismic sensors and geological data to forecast volcanic eruptions. The objective is to predict both the locations and the intensity of eruptions with the assistance of Watson's capabilities. Such applications have the potential to save lives and prevent loss in areas surrounding active volcanoes by providing early warnings and enabling proactive measures.
Hurricanes
Each year, hurricanes result in significant property damage amounting to millions of dollars. Consequently, meteorological departments are actively seeking improved methods for predicting and tracking natural disasters like hurricanes and cyclones, including their trajectory and intensity. Enhanced prediction techniques hold the potential to not only save more lives but also reduce property damage.
In a collaboration between NASA and Development Seed, Hurricane Harvey was tracked using satellite imagery and machine learning. This innovative approach proved to be six times more effective than conventional methods, enabling hourly tracking of the hurricane instead of the usual six-hour intervals. As a result, these technological advancements are proving instrumental in monitoring hurricanes and accurately forecasting their paths, thus aiding in effective mitigation efforts.
Limitations
Despite the advanced capabilities of artificial intelligence, its limitations hinder its widespread use in real-world applications. One major drawback is that while AI can outperform humans in terms of volume and speed of operations, it falls short in the quality of predictions. There are instances where AI can make errors, particularly due to flawed data fed into the system, collected by humans. Consequently, the results produced by AI may lack accuracy. Moreover, a concerning trend in the adoption of AI is our tendency to become overly reliant on it. Even if AI makes a mistake, there is a risk that we might still blindly trust the system and take actions accordingly. This blind trust can have severe consequences, as demonstrated in scenarios like earthquake aftershock predictions, where inaccuracies could lead to significant loss of life.
Another issue with AI lies in its reliance on historical data from past records of natural disasters. This means that AI-powered applications struggle to adapt to changing trends and magnitudes in events like floods and earthquakes. Furthermore, the current AI systems are unable to incorporate the impact of climate change on natural disasters, as they are trained solely on historical data. Consequently, predicting long-term trends of various natural disasters influenced by climate change remains a significant challenge for artificial intelligence. To ensure reliable and real-life implementation, researchers must continue to conduct numerous tests to address these limitations in AI technology.
Applications:
The integration of cutting-edge technologies like artificial intelligence and machine learning holds great promise for predicting natural disasters. However, it is crucial to address the existing limitations of these technologies before implementing them in real-world applications. Therefore, researchers must focus on resolving the current issues with artificial intelligence.
To ensure successful deployment, government organizations require a well-defined roadmap that streamlines the adoption process. This roadmap should include the following steps:
- Hiring experienced researchers and technology experts with expertise in AI.
- Collecting high-quality data to train AI-powered applications effectively.
- Enlisting skilled professionals to devise adoption strategies.
- Updating the existing infrastructure within the government organization to accommodate AI implementation.
- Providing education and training to government personnel on artificial intelligence.
By adopting AI for predicting natural disasters, millions of lives can be saved. Moreover, the datasets analyzed by AI-powered systems can enhance our understanding of the scale and patterns of various natural disasters, such as floods, earthquakes, and tsunamis. This knowledge can contribute to improved infrastructure planning in disaster-prone regions. Hence, it is essential for government organizations to embrace AI for accurate prediction and monitoring of natural disasters, thereby ensuring the safety and well-being of their citizens.
Source: Naveen Joshi. (2019,
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