AI can improve catastrophe management. Realizing its benefits requires recognizing and overcoming its limits.
FREMONT, CA: Acute natural disasters, such as those caused by the atmosphere, hydrology, geophysics, oceanography, or biology, are capable of wreaking havoc on human society as well as the natural world and even further afield. These kinds of disasters can have a disproportionately negative influence on particular places. Experts in geoscience and catastrophe risk mitigation frequently refer to occurrences like this as natural disasters because of the disproportionately adverse effects they have on certain people and regions.
In recent months, there has been a rise in interest in applying cutting-edge technologies, such as artificial intelligence (AI), to improve the management of natural disasters. It is believed that such technologies can also benefit natural emergency preparedness by capitalizing on a wealth of geospatial data to improve our understanding of natural catastrophic events, the promptness of detections, the accuracy and lead times of forecasts, and the efficiency of emergency communications.
Successes and limitations to data
High-quality data is the cornerstone of every AI-based strategy. Several restrictions and technological problems are considered when curating data for AI-based algorithms. If a training dataset includes many events, AI-based techniques may be quite effective. However, because of the rarity of these incidents, there is a limited amount of data available. Making synthetic data based on a physical comprehension of these threats is one approach.
Success and limitations to AI development
When high-quality databases are provided, AI-based algorithms combine several sources of information or modeling approaches to identify or predict occurrences. A deep learning method can combine seismic source and propagation modeling to produce probabilistic estimates of earthquake shaking intensity at a specific location.
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Challenges and solutions to AI implementation
In most cases, AI-based algorithms are designed in an academic setting by specialists in geoscience or machine learning to advance the scientific knowledge of natural calamities. Communication with decision-makers and end consumers (such as government emergency management agencies and humanitarian organizations) is frequently restricted during a research project's lifecycle, beginning with the acquisition of funding and continuing through the dissemination of the project's findings. For example, once a program is over, the results are presented at scientific conferences, in specialized committees, and peer-reviewed publications; nevertheless, these results rarely reach the stakeholders and end-users previously mentioned. Because of this gap, the implementation of these AI-based algorithms is hampered.
Innovative sources of information and AI-based technologies improve global disaster monitoring, prediction, and communication. Their deployment is typically hampered by minimal interaction between AI developers and practitioners and a lack of defined rules for designing, assessing, and applying these technologies.
