This study demonstrates that machine learning models, particularly Random Forest Classifiers, can effectively simulate and classify precipitation and extreme weather patterns across North Indian states, offering valuable insights for disaster preparedness and water resource management.
Tandon, Aayushi; Awasthi, Amit; Pattnayak, Kanhu Charan. 2025-03-25. Efficacy of Machine Learning in Simulating Precipitation and Its Extremes Over the Capital Cities in North Indian States. https://www.nature.com/articles/s41598-024-84360-w (AI & Environment Resource Hub; record paper-174; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Climate and greenhouse gases; Water; Biodiversity and ecosystems; Weather, hazards, and adaptation; Built environment and infrastructure
This course will help you understand AI's climate implications and identify practical next steps within your organization. The course begins with demystifying the connection between AI, Large Language Models (LLMs), data centers, and energy and water demand. Then you will learn about AI's environmental footprint, the related environmental and community impacts, you will evaluate real-world applications of AI across climate adaptation, energy transition, and nature conservation, and understand the business and policy landscape shaping corporate decisions.
Coursera
Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation; Built environment and infrastructure
AI is already reshaping the way we live and work, but what role can it play in tackling the effects of climate change? This short, practical course analyses how we can harness the power of AI to drive climate solutions, from planning resilient cities to protecting nature and powering the energy transition. Along the way, you'll hear from Stanford experts, analyse global case studies and reflect on what this could mean in a public sector context.
Apolitical
Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation; Built environment and infrastructure
This study presents an AI-driven framework that uses Ant Colony Optimisation, species-specific thermal traits, and high-resolution climate simulations to strategically place and select urban trees, achieving significant reductions in extreme heat and improved thermal comfort at the neighbourhood scale.
ScienceDirect
Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation; Built environment and infrastructure
This study uses random forest-based AI and Landsat imagery to assess how urban growth in Baghdad from 1985 to 2021 has significantly increased land surface temperatures, primarily due to reduced vegetation and moisture.
