Risk Heat Map

Risk heat maps are visual representations of risk levels across a defined area, aiming to identify high-risk zones and inform decision-making. Current research focuses on developing sophisticated models, including deep neural networks and machine learning algorithms like random forests and LightGBM, to generate these maps from diverse data sources such as sensor data, meteorological information, and driver behavior. Applications range from improving autonomous vehicle safety and driver warning systems to predicting disease prognosis and assessing environmental hazards like lightning strikes and heat stress, ultimately enhancing safety and informing preventative measures. The interpretability and accuracy of these risk assessments are key areas of ongoing investigation.