Technological Platform for Hydrogeological Risk Computation and Water Leakage Detection based on a Convolutional Neural Network

An effective method of managing water pipelines is essential for maximizing water efficiency and preserving water quality for both human and agricultural use. The aim of this study is to determine and test such a method for large-scale water piping networks intended for irrigation. Despite the considerable efforts being made worldwide to reduce the waste of potable water, nowadays there is still no widely used technique for this purpose. The most common methodologies for the detection of leaks use acoustic emission, gas detection, various types of sensors, such as hydraulic pressure and temperature. Unfortunately, these methods present critical issues and are difficult to apply on a large scale. This paper proposes an approach based on infrared thermography that can be associated with the use of a drone in order to reach and monitor large areas in a short time. A real time monitoring and decision support technological platform oriented towards water efficiency and the computation of hydrogeological risks has been implemented. In fact, the system integrates different detection technologies, as the Infrared Thermographic (IRT) technique is supported by the Ground Penetrating Radar (GPR) for the inspection of buried pipelines. Artificial Intelligence (AI) algorithms have been used for radiometric image processing with the aim of identifying water leaks even when these are latent. In particular, a Convolutional Neural Network (CNN) was implemented to identify leakages. As regards the calculation of hydrogeological risk, algorithms based on logical conditions have been implemented that allow to match the meteorological data and the hydrogeological risk maps of the places of interest, in order to predict the hydrogeological risk of the territory.