The environment and ecosystem are self-sustaining in nature. Any imbalance can threaten the life on earth as well as the planet itself. In the aftermath of industrialization, large-scale deforestation and biodiversity losses have devastated our planet. Before embracing industry 4.0, it is time that we use the power of artificial intelligence (AI) for improved weather prediction and disaster management to reduce impact of natural catastrophes.
AI has been pivotal in improving our decision-making capabilities. Numerous technological applications such as smart grid initiatives, electric vehicles, medical sciences, and personal safety form the cortex of AI. However, use of AI is in weather prediction and detection of anomalies is astounding. Weather prediction itself is a complicated science. It requires real-time analysis of the data gathered through remote devices.
Using AI for weather control
Numerous satellites and sensors monitor climate change and geo-physical activites remotely. Meteorologists combine this information with power software to predict daily weather, volcanic eruptions, hurricanes, earthquakes etc. As weather can change quickly, the reliability of such predictions are effective for 5-7 days only. Additionally, if the time span between data collection and analysis exceed this limit then the prediction can become less accurate.
Therefore, in order to improve accuracy irrespective of time span it is essential to combine artificial intelligence with machine learning (ML) algorithms. There are dedicated cloud spaces available for safe storage of data collected from sensors. Various AI tools can be used for processing of this data. But in order to make sense of all the information power data recognition algorithms using machine learning can be very helpful.
For a better tomorrow
It is time that we obtain a deep understanding of the geo-physical phenomena using powerful computational software and use this knowledge to choose the appropriate AI solution. A strong integration of AI-ML can help in early detection of natural catastrophes and succinct decisions on minimizing the loss of life.
There are already some paper regarding potntila use of ML into seismic waves pattern interpretation. I guess SEETALABS can do something and very soon