Machine Learning Applications in Predicting Climate Change Patterns
Muhammad Asiel · 2025-02-17
This paper reviews how machine learning is being used to forecast climate change impacts, such as temperature shifts, extreme weather, sea level rise, and greenhouse gas emissions, by analyzing complex climate and remote-sensing data. It highlights ML’s potential to improve predictability and support climate adaptation and policy decisions, while noting ongoing challenges in data quality, model explainability, and computational limits.
Muhammad Asiel. 2025-02-17. Machine Learning Applications in Predicting Climate Change Patterns. https://rjsaonline.org/index.php/ComputeX/article/view/13 (AI & Environment Resource Hub; record paper-406; collection snapshot 2026-09-15).
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Joyeeta Das is a prominent climate and deep-tech entrepreneur, currently serving as the CEO and Co-founder of Samudra Oceans, a London-based company focused on scaling seaweed farming using robotics and AI for carbon capture and ocean health. She is a serial entrepreneur with a strong background in technology and a focus on leveraging innovation for social good. Prior to Samudra Oceans, she founded GYANA, an organization democratizing AI with a no-code platform, and led SuperPitch to acquisition by Diversity X. Discuss how AI, climate change, and women empowerment can come together with this podcast episode.
youtube.com
Shared topics: Climate and greenhouse gases; Oceans and coasts; Weather, hazards, and adaptation
Severe flash floods in Charikar, Afghanistan, on August 26, 2020, were driven by extreme atmospheric instability, deep low-level convergence, and local topography, with climate change intensifying frontal activity and baroclinicity in the region.
Journal of Water and Climate Change
Shared topics: Governance and society; Climate and greenhouse gases; Weather, hazards, and adaptation
We are happy to share this strategic report–AI and Climate Change: The Global South Facing the New Geopolitics of Innovation–written by Lori Regattieri and jointly launched by CIPÓ and the Green Screen Coalition. The report articulates key connections between energy infrastructures, climate justice and purported AI solutions for the “energy transition”. Their work argues for multilateral, pluriversal, and multisectoral approaches to digital infrastructure and industrial policy. The report names the violence of the “logic of expansion” and the fallacies of transition, which “simultaneously drives energy regimes and artificial intelligence systems”. Lori’s critique on the entanglement of many different social and political issues in Brazil is critical in a time where the technology industry lays the groundwork to be the saviors of COP30 in Belem, Brazil. With COP less than two months away, the report provides a powerful framing for those curious about the industrial dynamics in the region, and the ontological logic underpinning them.
Green Screen Coalition
Shared topics: Governance and society; Climate and greenhouse gases; Oceans and coasts
AI and ML optimize optoelectronic systems for sustainability by enhancing energy efficiency, renewable energy harvesting, environmental monitoring, and smart cities while also advancing ocean optics and photonics for marine ecosystem monitoring and climate change mitigation.
