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Climate change poses a critical challenge to global ecosystems and human societies. Accurate prediction and analysis of climate trends are essential to mitigating its adverse effects and formulating sustainable policies. This project aims to develop a predictive model that leverages machine learning algorithms to forecast climate change using historical weather data. By analyzing key meteorological factors such as temperature, humidity, rainfall, and atmospheric pressure, the system identifies patterns that contribute to long-term climate variability. The project seeks to create a more efficient and accessible tool for climate prediction, enhancing decision-making capabilities for stakeholders. Through this approach, we aim to provide reliable insights that support sustainable environmental strategies.
Keywords:
To predict climate for main region as well as for sub regions
Cite Article:
"EXPLAINABILITY IN AL FOR ENVIRONMENTAL SUSTAINABILITY: INTERPRETING MODELS FOR CLIMATE CHANGE PREDICTIONS", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.a155-a160, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503018.pdf
Downloads:
000334
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator