Explainable Artificial Intelligence for Atmospheric Research
A special issue of Atmosphere (ISSN 2073-4433).
Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 632
Special Issue Editor
Interests: ozone; remote sensing; atmospheric physics; atmospheric pollution; climate dynamics; paleoclimate; OSL dating
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are launching a Special Issue dedicated to the role of explainable artificial intelligence (AI) in atmospheric research.
Over the last 5 to 10 years, artificial intelligence has arisen as a promising tool that has been implemented in many different fields of research. A major advantage of AI is that it offers many techniques and tools that can be used not only for processing big data but also for the development of algorithms and applications. A common feature of AI applications is that, in some sense, they can be self-developed. Historically, AI has been predominantly associated with robotics and the human effort to develop robots with outstanding capabilities, and, of course, reaching the target of developing humanoid robots. Nowadays, AI has been expanded to other directions, such as in environmental research, climate dynamics, and atmospheric research.
It is vital that new tools, such as AI and its subsets, e.g., machine learning (ML), become available to scientists because they provide us with the chance to try new things and study our fields of research in more detail. However, in the case of AI, there is also danger. The danger is associated with the AI’s self-development characteristic. For example, I can “feed” a neural network with air temperature and other data of a specific location to create a model tool that can forecast the temperature in this area. How do I treat this model tool? Can I use it, as it is, for further research or should I firstly shay light in this “black box”, in order to understand the modeled mechanisms, and use it afterwards?
This Special Issue is open to any research work-related, directly or indirectly, to the implementation of AI in atmospheric research and the aforementioned concerns. The listed keywords suggest just a few of the many possibilities.
Dr. John Christodoulakis
Guest Editor
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Keywords
- artificial intelligence vs. explainable artificial intelligence
- machine learning
- remote sensing
- big data
- air pollution
- atmospheric physics
- corrosion and soiling due to air pollution and other environmental factors
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