![[2602.00240] Green-NAS: A Global-Scale Multi-Objective Neural Architecture Search for Robust and Efficient Edge-Native Weather Forecasting](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.00240] Green-NAS: A Global-Scale Multi-Objective Neural Architecture Search for Robust and Efficient Edge-Native Weather Forecasting
Summary
Green-NAS presents a multi-objective neural architecture search framework aimed at optimizing weather forecasting models for low-resource environments while minimizing energy costs and carbon footprints.
Why It Matters
As climate change intensifies, efficient weather forecasting becomes crucial. Green-NAS addresses the need for sustainable AI solutions by optimizing model performance and resource use, making it relevant for both researchers and practitioners in machine learning and environmental science.
Key Takeaways
- Green-NAS optimizes neural architecture for efficient weather forecasting.
- The framework adheres to 'Green AI' principles, minimizing energy consumption.
- Achieved significant accuracy with fewer model parameters compared to traditional models.
- Transfer learning enhances forecasting accuracy in low-data scenarios.
- The approach supports sustainable AI deployment in resource-constrained settings.
Computer Science > Machine Learning arXiv:2602.00240 (cs) [Submitted on 30 Jan 2026 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Green-NAS: A Global-Scale Multi-Objective Neural Architecture Search for Robust and Efficient Edge-Native Weather Forecasting Authors:Md Muhtasim Munif Fahim, Soyda Humyra Yesmin, Saiful Islam, Md. Palash Bin Faruque, Md. A. Salam, Md. Mahfuz Uddin, Samiul Islam, Tofayel Ahmed, Md. Binyamin, Md. Rezaul Karim View a PDF of the paper titled Green-NAS: A Global-Scale Multi-Objective Neural Architecture Search for Robust and Efficient Edge-Native Weather Forecasting, by Md Muhtasim Munif Fahim and 9 other authors View PDF HTML (experimental) Abstract:We introduce Green-NAS, a multi-objective NAS (neural architecture search) framework designed for low-resource environments using weather forecasting as a case study. By adhering to 'Green AI' principles, the framework explicitly minimizes computational energy costs and carbon footprints, prioritizing sustainable deployment over raw computational scale. The Green-NAS architecture search method is optimized for both model accuracy and efficiency to find lightweight models with high accuracy and very few model parameters; this is accomplished through an optimization process that simultaneously optimizes multiple objectives. Our best-performing model, Green-NAS-A, achieved an RMSE of 0.0988 (i.e., within 1.4% of our manually tuned baseline) using only 153k model parameters, which is 239 times fe...
