International Journal of Contemporary Research in Multidisciplinary

Abstract:

Artificial Intelligence (AI) is believed to have significant potential use in tackling. Through the use of relevant data to build an algorithm, machine learning primarily aims to automate human help in terms of applied climate change. A subset of artificial intelligence (AI), machine learning focuses on the development of systems that can learn from past data for climate change in the platform of AI. There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. Here, we introduce a systematic framework for describing the effects of machine learning (ML) on environmental gas emissions, encompassing three categories computing computing-related impacts, immediate impacts of applying ML, and system-level impacts. Using this framework, we identify priorities for impact assessment and climate change scenario analysis and suggest policy levels for better understanding and multiple shaping of the effects of ML on climate change mitigation. In this review paper, we combine AI and machine learning procedures. Also, we are used to this review paper of applied ML for multiple climate change regression. Climate change. This paper explores the connections between AI and climate change research as a whole and its usefulness in climate change adaptation efforts in particular. Using a systematic review of the literature on applications of AI for climate change adaptation and a questionnaire survey of a multinational and interdisciplinary team of climate change researchers, this paper shows the various means via which AI can support research on climate change in diverse regions and contribute to efforts towards climate change adaptation. Through the use of relevant data to build an algorithm, machine learning primarily aims to automate human help in terms of applied climate change. A subset of artificial intelligence (AI), machine learning focuses on development.

Keywords:

Climate Change, Artificial Intelligence (AI), Sustainable Development, Machine Learning, Sustainability

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