Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

"AI-based models are revolutionizing weather forecasting and have surpassed leading numerical weather prediction systems on ...

Dhruv Shenai investigates how machine learning and lab automation are transforming materials science at Cambridge ...

Modern agriculture is a data-rich but decision-constrained domain, where traditional methods struggle to keep pace with ...

Buildings produce a large share of New York's greenhouse gas emissions, but predicting future energy demand—essential for ...

Environmental and sustainability experts explain that, in principle, artificial intelligence (AI) is a branch of computer ...

Machine learning (ML) is rapidly emerging as a powerful tool to improve the safety, reliability, and long-term performance of marine structures exposed to harsh ocean environments. This study presents ...

This project focuses on analyzing rainfall data across Indian subdivisions, cleaning and preprocessing the dataset, engineering seasonal features, visualizing rainfall trends, and building machine ...

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Individual prediction uncertainty is a key aspect of clinical prediction model performance; however ...

Introduction: This study investigated the relationships between short-duration heavy rainfall (SDHR) events and lightning activity over Guangxi, China, during the pre-summer rainy season from 2019 to ...

Aiming to become the ‘Uniswap’ of prediction markets, Rain utilizes an AI oracle that tackles bottlenecks and is supported by a unique dispute mechanism, making prediction markets faster, more ...