An Improved AI Weather Prediction System

Abstract

Weather prediction remains a critical challenge due to the nonlinear and dynamic nature of atmospheric systems, as traditional numerical weather prediction (NWP) models struggle to process large, high-dimensional meteorological data and often lack the adaptability needed for accurate short- and medium-term forecasts, particularly during extreme weather events. This study develops an improved AI-driven weather prediction system that enhances forecasting accuracy for temperature, humidity, wind speed, and atmospheric pressure through a Random Forest predictive model integrated with the Open Weather Map API and geolocation services. Using Agile methodology, data was collected, preprocessed, and trained in Python, while a web-based interface was built with JavaScript/TypeScript and React for visualization. The proposed system achieved approximately 87% short-term forecasting accuracy (87% applies to 1 – 7-day short term forecast), demonstrating improved precision and enhanced early-warning capability for extreme events. Comparative evaluation showed that, whereas the existing edge-based system was constrained by low processing power, maintenance overhead, and security concerns, the proposed AI system outperformed it in accuracy, adaptability, and real-time usability, offering significant benefits for agriculture, disaster management, and urban planning.