Using AI Modeling to Predict the Impact of Cloud Seeding on Amount of Rainfall and How that Affects the Temperature in Different Regions
Rex Carvalho, Ihita Mandal
Affiliation: Gems Wellington International School
IJSCAR Vol. 2, Issue 1 (2025) · pp. 33–36
Abstract
Water is one of the most essential resources on Earth yet many regions face severe water scarcity due to growing populations increased land use and climate change. To address this challenge we employed cloud seeding as a promising technique for augmenting water supplies. While prior research has primarily focused on increasing precipitation this study introduces a novel angle by examining how cloud seeding-induced rainfall may also influence regional temperature regulation over time. This dual-purpose investigation is particularly relevant in arid regions like the Middle East where water scarcity and extreme heat coexist. Using historical datasets and AI-based modeling including linear regression and neural networks we analyzed the relationship between rainfall and temperature across different regions. Results revealed a consistent negative correlation between rainfall and temperature suggesting that cloud seeding may have cooling effects in addition to boosting precipitation. This study presents early evidence that weather modification could play a role in mitigating long-term climate stress offering a new perspective on the benefits of cloud seeding.
Keywords: Cloud seeding, Linear regression, Neural networks, Rainfall