Implementation of IoT Data Fusion Architectures for Precipitation Forecasting

This article explores the implementation of data fusion architectures in IoT systems for precipitation forecasting. IoT networks enable the collection of large volumes of real-time environmental data, which, when combined through data fusion techniques, provide a cohesive and comprehensive dataset for analysis. Using MongoDB as a storage and processing platform, a temporal fusion approach was implemented to analyze seasonal trends and recurring patterns in environmental data. The study highlights the challenges of integrating heterogeneous data, including the presence of outliers, and proposes solutions based on advanced data analysis and machine learning techniques. Data preprocessing techniques, such as outlier detection and normalization, were applied to enhance data quality before fusion. Results demonstrate that temporal fusion, combined with machine learning, significantly improves the accuracy and efficiency of precipitation forecasting systems. Key techniques such as Random Forest were employed, and performance was evaluated using metrics like MAE, MSE, R², and Cross-Validation MAE (CV\_MAE). The findings indicate that temporal fusion, especially when combined with exponential smoothing, surpasses other methods, providing a robust approach to precipitation forecasting.