This article presents the design and deployment of ClimaBogotá v1.2, a climate prediction system tailored for high-altitude urban micro-zones in Bogotá, Colombia. The system combines low-cost IoT sensing, machine learning modeling, and cloud-based orchestration to enable scalable and affordable meteorological forecasting. Its architecture comprises Raspberry Pi-based weather stations, a Random Forest model trained on engineered temporal features, and an n8n-driven automation pipeline for real-time inference and dissemination via Telegram, PostgreSQL, and Grafana. With a Mean Absolute Error of 2.59 °C and an R2 of 0.6286 on a 30 min forecast horizon, the system demonstrates both predictive reliability and operational feasibility using free-tier cloud resources. Unlike traditional weather systems, ClimaBogotá emphasizes modularity, adaptability, and cost-efficiency, offering a replicable framework for decentralized climate monitoring in data-scarce urban environments. Temporal misalignment between sensor nodes was identified as the primary constraint, informing future enhancements toward distributed learning strategies.

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Climate prediction,IoT,Machine learning,Micro-zone,n8n,Time series,Workflow orchestration

• Ciências da Computação e da Informação - Ciências Naturais
• Outras Engenharias e Tecnologias - Engenharia e Tecnologia