Integrating Radar Nowcasting and Machine Learning in an Advanced Early-Warning System for Milan’s Hydraulic Node

In recent decades, climate change has led to a significant increase in the frequency and intensity of extreme weather events, such as heavy rainfall and flash floods, resulting in higher hydrogeological risk and increased vulnerability of both ecosystems and urban infrastructures. These phenomena, characterized by strong spatial and temporal variability, are particularly impactful in urban areas, where impervious surfaces, high population density, and the presence of critical infrastructure amplify consequences of flooding and inundation.

The hydraulic system of Milan represents a critical case study: natural watercourses and artificial canals are closely intertwined with the urban fabric. In particular, floods of the River Seveso recurrently cause inundation in the northern part of the city, producing widespread damage to people, infrastructure, and mobility.

In this context, the ability to accurately forecast meteorological and hydrological variables at very short lead times is crucial for risk management and the development of timely early-warning systems. This study proposes the use of machine learning models, such as LDCast and GPTCast, developed by MeteoSwiss and the Bruno Kessler Foundation in Trento, respectively, for radar-based nowcasting. The estimates produced by these models are subsequently coupled both as input for physically based hydrological models and within artificial intelligence algorithms developed by the Politecnico di Milano.

The objective of the study is to evaluate the overall performance of this forecasting system and to demonstrate how it might represent a significant advancement in the implementation of very short-term early-warning systems.