FIWARE-Ready: IMREDD - University Côte d'Azur - Noise Forecasting (Artificial Intelligence,Big Data,Data Analytics,Internet of Things,Software as a Service)

This service aims to provide short-term forecasting of a time series such as noise. It builds on historical data stored in a CSV file with a column for time, and a column for the values, then, a forecast model is trained, and forecasts are made available on demand through an API.

The Noise forecasting service is designed to assist cities and citizens in determining the time at which there will most likely be high noise annoyance around the location of a sensor. By leveraging historical data and considering various factors such as day of the week, and past noise evolution patterns, the model aims to estimate the future noise at a specific location and time. The service employs standard AI algorithms to collect and process data, ensuring accurate predictions. These algorithms build on historical noise data, identifying patterns and trends that contribute to noise intensity fluctuations. By understanding these patterns, the model can generate reliable forecasts of noise levels. The noise forecasting service offers a valuable solution for citizens, and forecasts are available in different format through an API.

As cities embrace the concept of smart cities, the noise forecasting service plays an important role in addressing the challenges and needs of citizens. One of the key challenges is to provide a safe and pleasant environment to people living in cities. By accurately forecasting noise levels, the service enables citizens to avoid going out at times of high noise, or it enables cities to change the way people use their transportation system in order to reduce the impact of traffic on noise.

IMREDD - University Côte d'Azur Noise Forecasting