I proposed Rough Set based uncertainty measures (span) (Yadav, 2021) for various decision systems in my post-Ph.D. work, which is an impressive contribution to the field. This research work can improve decision-making processes and enhance the reliability of decision systems. The model developed uncertainty measures for unsupervised multi-category decisions for structured and unstructured data. The problem is computationally complex, and we proposed a solution based on evolutionary algorithms to solve it in real-time. The formulated methods were generic. The specific application of these methods to relief and rescue dispatch, addressing both minor and significant climate impacts, was illustrated. Examples provided the theoretical explanation. If given the opportunity, I would like to extend these concepts to real-time applications using data from past and present climate disaster relief/rescue efforts and prepare the model for future natural calamities.
Furthermore, I would in the future also like to develop measures for semi-supervised decision-making, incorporating other decision-making techniques or human guidance with these uncertainty measures. I also want to implement these theoretical concepts into software for real-time decision-making to work with human decision-making during natural disasters. Henceforth, it can improve with consistent human feedback. The proposed approach can apply to natural or non-natural events such as explosions, bomb blasts, artificial disaster relief, and management-based decision-making questions. Studying and solving formulated problems via other techniques is another area of future work where I would like to work. I would also like to study other ways to develop span-based uncertainty measures and other decision-making systems to devise more robust decision-making for my dedicated interest in climate change initiatives.
Preparing for human safety and saving lives is crucial since climate change cannot be controlled in the short term. Despite sophisticated weather monitoring systems, natural calamities such as heavy rains, cloud bursts, and wildfires are predicted poorly in advance. AI-based systems have the potential to predict even the mildest of climate calamities. As a future work, deep learning can be utilized to predict sudden events. It is essential to develop sophisticated systems for accurate forecasting to save lives. Applying this in real-time to climate data can revolutionize disaster management and improve community safety. I have also proposed a Fuzzy Expert System-based alarm for predicting natural disasters. The aim is to alert authorities that AI has detected an extreme condition. Climatologists can use AI to develop an Expert System for accurately predicting climate patterns and potential natural disasters. I believe that by leveraging the power of AI and the expertise of climatologists, we can create highly effective systems that can help make the world safer.
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Artificial Intelligence, Computer Science & Mathematics 