(untitled) (International Association of Biologicals and Computational Digest, 2023)
The impact of climate change is a growing concern for many societies, and there is a pressing need for accurate and reliable models that can predict the future of the climate system. Using artificial intelligence (AI) methods like machine learning and deep learning to evaluate and model climate data is one potential strategy. With an emphasis on statistical, mathematical, Python, and GIS-based studies on the Gujarat coastline, this study offers a summary of the current state of AI applications in climate change research. We highlight the promise of these techniques for expanding our understanding of climate change and creating efficient ways for tackling it as we present instances of AI techniques applied in climate modelling, environmental monitoring, and weather forecasting. The paper gives a thorough list of pertinent references for people who want to learn more about how statistical, mathematics, GIS, and artificial intelligence techniques are used in climate change research. These sources cover a wide range of subjects, including the use of neural networks in climate modelling and the use of machine learning techniques for the study of satellite data. The paper serves as a useful resource for anybody wishing to further study the interface of AI and climate change research by giving this list of resources. With the help of these references, readers can get knowledge of the most recent advancements in the subject and see examples of how AI methods are being applied to improve our understanding of climate change and its effects.