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- A Machine Learning-based Decision Support System for IoT Monitoring SolutionsPublication . Ladeira, Edgar Filipe Loureiro; Silva, Bruno Miguel Correia daGlobally, environmental incidents occur daily due to the emission of pollutant gases, many of which are the main causes of environmental disasters and premature deaths. The significant impact of these gases on public health is a major concern, especially in rural regions. Inhalation of these compounds often affects the daily lives and health of people living in these areas, often without their awareness of the risks involved. The goal of this project is to develop a decision support system powered by Artificial Intelligence (AI) and a robust platform for visualizing statistics on pollutant gases. Using an Internet of Things (IoT) ecosystem, the system aims to notify residents about environmental conditions and prevent any risks, including exposure to hazardous gases. Data is collected by digital sensors, aggregated by a microprocessor, and transmitted to a centralized database. This database feeds into the digital platform, which provides comprehensive and easy-tonavigate graphs, helping users to clearly understand pollution levels. These visualizations assist authorities and users in making informed decisions about environmental safety. This work aims to find the best Machine Learning (ML) model for this type of data (time series), in order to generate accurate predictions, focusing on the exploration of various ML techniques. The AI-based decision support system uses ML techniques to analyze the collected data and provide assessments of hazardous gas levels, as well as future forecasts of these levels, using unidirectional LSTM layers overlaid on bidirectional LSTM layers, an architecture that has shown the best results. In areas where pollutant gases such as radon are common, these forecasts enable the generation of timely alerts and recommendations to reduce health risks. Overall, this study contributes to the development of an innovative system through the analysis and processing of sensory data, such as pollutant gases and environmental variables. In a real-world context, the forecast is integrated into the RuraLTHINGS platform, providing system users with information to make more informed decisions about their indoor spaces, with the goal of promoting better living conditions.