Forecasting Short-Term Indoor Radon: A Machine Learning Approach Using LSTM Networks - IEEE Publication
August 15th, 2023 | 12 minutes read
Explore the realm of indoor radon forecasting in Implementing LSTM-Based Approaches for Indoor Radon Forecasting. Delving into Machine Learning (ML) techniques, this work evaluates two LSTM-based methods for predicting future indoor radon levels, crucial for managing exposure risk. The study unveils promising preliminary results, highlighting the effectiveness of the Long Short-Term Memory (LSTM) algorithm in providing accurate forecasts across different time windows. These findings offer insights into using ML as a valuable tool for Indoor Air Quality management, aiding in preventive measures to address health risks associated with indoor radon exposure.